2021
| (J) | Alexandros Papadopoulos, Fotis Topouzis and Anastasios Delopoulos
Scientific Reports, 2021 Jul
[Abstract][BibTex][pdf] Diabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for monitoring their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to retinopathy severity estimates for patients in remote regions or even for complementing the human expert’s diagnosis. Here we propose a machine learning system for the detection of referable diabetic retinopathy in fundus images, which is based on the paradigm of multiple-instance learning. Our method extracts local information independently from multiple rectangular image patches and combines it efficiently through an attention mechanism that focuses on the abnormal regions of the eye (i.e. those that contain DR-induced lesions), thus resulting in a final image representation that is suitable for classification. Furthermore, by leveraging the attention mechanism our algorithm can seamlessly produce informative heatmaps that highlight the regions where the lesions are located. We evaluate our approach on the publicly available Kaggle, Messidor-2 and IDRiD retinal image datasets, in which it exhibits near state-of-the-art classification performance (AUC of 0.961 in Kaggle and 0.976 in Messidor-2), while also producing valid lesion heatmaps (AUPRC of 0.869 in the 81 images of IDRiD that contain pixel-level lesion annotations). Our results suggest that the proposed approach provides an efficient and interpretable solution against the problem of automated diabetic retinopathy grading. @article{alpapado2021, |
| (J) | Konstantinos Kyritsis, Petter Fagerberg, Ioannis Ioakimidis, K Ray Chaudhuri, Heinz Reichmann, Lisa Klingelhoefer and Anastasios Delopoulos
"Assessment of real life eating difficulties in Parkinson’s disease patients by measuring plate to mouth movement elongation with inertial sensors"
Scientific Reports, 11, pp. 1-14, 2021 Jan
[Abstract][BibTex][pdf] Parkinson’s disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease’s natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia—factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite’s PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD. @article{kyritsis2021assessment, |
2021
| (C) | Athanasios Kirmizis, Konstantinos Kyritsis and Anastasios Delopoulos
"A Bottom-up method Towards the Automatic and Objective Monitoring of Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors"
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021 Dec
[Abstract][BibTex][pdf] @conference{kirmizis2021bottom, |
2020
| (J) | Alexandros Papadopoulos , Dimitrios Iakovakis, Lisa Klingelhoefer, Sevasti Bostantjopoulou, K. Ray Chaudhuri, Konstantinos Kyritsis, Stelios Hadjidimitriou, Vasileios Charisis , Leontios J. Hadjileontiadis and Anastasios Delopoulos
Scientific Reports, 2020 Dec
[Abstract][BibTex][pdf] Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment. @article{alpapado2020, |
| (J) | Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos
"A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches"
IEEE Journal of Biomedical and Health Informatics, 2020 Apr
[Abstract][BibTex][pdf] The increased worldwide prevalence of obesity has sparked the interest of the scientific community towards tools that objectively and automatically monitor eating behavior. Despite the study of obesity being in the spotlight, such tools can also be used to study eating disorders (e.g. anorexia nervosa) or provide a personalized monitoring platform for patients or athletes. This paper presents a complete framework towards the automated i) modeling of in-meal eating behavior and ii) temporal localization of meals, from raw inertial data collected in-the-wild using commercially available smartwatches. Initially, we present an end-to-end Neural Network which detects food intake events (i.e. bites). The proposed network uses both convolutional and recurrent layers that are trained simultaneously. Subsequently, we show how the distribution of the detected bites throughout the day can be used to estimate the start and end points of meals, using signal processing algorithms. We perform extensive evaluation on each framework part individually. Leave-one-subject-out (LOSO) evaluation shows that our bite detection approach outperforms four state-of-the-art algorithms towards the detection of bites during the course of a meal (0.923 F1 score). Furthermore, LOSO and held-out set experiments regarding the estimation of meal start/end points reveal that the proposed approach outperforms a relevant approach found in the literature (Jaccard Index of 0.820 and 0.821 for the LOSO and held-out experiments, respectively). Experiments are performed using our publicly available FIC and the newly introduced FreeFIC datasets. @article{kyritsis2020data, |
2020
| (C) | Christos Diou, Ioannis Sarafis, Vasileios Papapanagiotou, Leonidas Alagialoglou, Irini Lekka, Dimitrios Filos, Leandros Stefanopoulos, Vasileios Kilintzis, Christos Maramis, Youla Karavidopoulou, Nikos Maglaveras, Ioannis Ioakimidis, Evangelia Charmandari, Penio Kassari, Athanasia Tragomalou, Monica Mars, Thien-An Ngoc Nguyen, Tahar Kechadi, Shane O' Donnell, Gerardine Doyle, Sarah Browne, Grace O' Malley, Rachel Heimeier, Katerina Riviou, Evangelia Koukoula, Konstantinos Filis, Maria Hassapidou, Ioannis Pagkalos, Daniel Ferri, Isabel Pérez and Anastasios Delopoulos
"BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment"
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2020 May
[Abstract][BibTex][pdf] Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO, a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants. @inproceedings{diou2020bigo, |
| (C) | Vasileios Papapanagiotou, Ioannis Sarafis, Christos Diou, Ioannis Ioakimidis, Evangelia Charmandari and Anastasios Delopoulos
"Collecting big behavioral data for measuring behavior against obesity"
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020 May
[Abstract][BibTex][pdf] Obesity is currently affecting very large portions of the global population. Effective prevention and treatment starts at the early age and requires objective knowledge of population-level behavior on the region/neighborhood scale. To this end, we present a system for extracting and collecting behavioral information on the individual-level objectively and automatically. The behavioral information is related to physical activity, types of visited places, and transportation mode used between them. The system employs indicator-extraction algorithms from the literature which we evaluate on publicly available datasets. The system has been developed and integrated in the context of the EU-funded BigO project that aims at preventing obesity in young populations. @inproceedings{papapanagiotou2020collecting, |
| (C) | Ioannis Sarafis, Christos Diou, Vasileios Papapanagiotou, Leonidas Alagialoglou and Anastasios Delopoulos
"Inferring the Spatial Distribution of Physical Activity in Children Population from Characteristics of the Environment"
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2020 May
[Abstract][BibTex][pdf] Obesity affects a rising percentage of the children and adolescent population, contributing to decreased quality of life and increased risk for comorbidities. Although the major causes of obesity are known, the obesogenic behaviors manifest as a result of complex interactions of the individual with the living environment. For this reason, addressing childhood obesity remains a challenging problem for public health authorities. The BigO project relies on large-scale behavioral and environmental data collection to create tools that support policy making and intervention design. In this work, we propose a novel analysis approach for modeling the expected population behavior as a function of the local environment. We experimentally evaluate this approach in predicting the expected physical activity level in small geographic regions using urban environment characteristics. Experiments on data collected from 156 children and adolescents verify the potential of the proposed approach. Specifically, we train models that predict the physical activity level in a region, achieving 81% leave-one-out accuracy. In addition, we exploit the model predictions to automatically visualize heatmaps of the expected population behavior in areas of interest, from which we draw useful insights. Overall, the predictive models and the automatic heatmaps are promising tools in gaining direct perception for the spatial distribution of the population's behavior, with potential uses by public health authorities. @conference{sarafis2020inferring, |
2019
| (J) | Alexandros Papadopoulos, Konstantinos Kyritsis, Lisa Klingelhoefer, Sevasti Bostanjopoulou, K. Ray Chaudhuri and Anastasios Delopoulos
IEEE Journal of Biomedical and Health Informatics, 2019 Dec
[Abstract][BibTex][pdf] Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algo- rithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings. @article{alpapado2019detecting, |
| (J) | Christos Diou, Ioannis Sarafis, Vasileios Papapanagiotou, Ioannis Ioakimidis and Anastasios Delopoulos
Statistical Journal of the IAOS, 35, (4), pp. 677-690, 2019 Dec
[Abstract][BibTex][pdf] The way we eat and what we eat, the way we move and the way we sleep significantly impact the risk of becoming obese. These aspects of behavior decompose into several personal behavioral elements including our food choices, eating place preferences, transportation choices, sleeping periods and duration etc. Most of these elements are highly correlated in a causal way with the conditions of our local urban, social, regulatory and economic environment. To this end, the H2020 project “BigO: Big Data Against Childhood Obesity” (http://bigoprogram.eu) aims to create new sources of evidence together with exploration tools, assisting the Public Health Authorities in their effort to tackle childhood obesity. In this paper, we present the technology-based methodology that has been developed in the context of The way we eat and what we eat, the way we move and the way we sleep significantly impact the risk of becoming obese. These aspects of behavior decompose into several personal behavioral elements including our food choices, eating place preferences, transportation choices, sleeping periods and duration etc. Most of these elements are highly correlated in a causal way with the conditions of our local urban, social, regulatory and economic environment. To this end, the H2020 project “BigO: Big Data Against Childhood Obesity” (http://bigoprogram.eu) aims to create new sources of evidence together with exploration tools, assisting the Public Health Authorities in their effort to tackle childhood obesity. In this paper, we present the technology-based methodology that has been developed in the context of BigO in order to: (a) objectively monitor a matrix of a population’s obesogenic behavioral elements using commonly available wearable sensors (accelerometers, gyroscopes, GPS), embedded in smart phones and smart watches; (b) acquire information for the environment from open and online data sources; (c) provide aggregation mechanisms to correlate the population behaviors with the environmental characteristics; (d) ensure the privacy protection of the participating individuals; and (e) quantify the quality of the collected big data. BigO in order to: (a) objectively monitor a matrix of a population’s obesogenic behavioral elements using commonly available wearable sensors (accelerometers, gyroscopes, GPS), embedded in smart phones and smart watches; (b) acquire information for the environment from open and online data sources; (c) provide aggregation mechanisms to correlate the population behaviors with the environmental characteristics; (d) ensure the privacy protection of the participating individuals; and (e) quantify the quality of the collected big data. @article{DiouIAOS2019, |
| (J) | Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos
IEEE Journal of Biomedical and Health Informatics (JBHI), 2019 Jan
[Abstract][BibTex][pdf] Overweight and obesity are both associated with in-meal eating parameters such as eating speed. Recently, the plethora of available wearable devices in the market ignited the interest of both the scientific community and the industry towards unobtrusive solutions for eating behavior monitoring. In this paper we present an algorithm for automatically detecting the in-meal food intake cycles using the inertial signals (acceleration and orientation velocity) from an off-the-shelf smartwatch. We use 5 specific wrist micromovements to model the series of actions leading to and following an intake event (i.e. bite). Food intake detection is performed in two steps. In the first step we process windows of raw sensor streams and estimate their micromovement probability distributions by means of a Convolutional Neural Network (CNN). In the second step we use a Long-Short Term Memory (LSTM) network to capture the temporal evolution and classify sequences of windows as food intake cycles. Evaluation is performed using a challenging dataset of 21 meals from 12 subjects. In our experiments we compare the performance of our algorithm against three state-of-the-art approaches, where our approach achieves the highest F1 detection score (0.913 in the Leave-One-Subject-Out experiment). The dataset used in the experiments is available at https://mug.ee.auth.gr/intake-cycle-detection/. @article{kyritsis2019modeling, |
| (J) | Langlet, Billy, Fagerberg, Petter, Delopoulos, Anastasios, Papapanagiotou, Vasileios, Diou, Christos, Maramis, Christos, Maglaveras, Nikolaos, Anvret, Anna, Ioakimidis and Ioannis
Nutrients, 11, (3), pp. 672, 2019 Mar
[Abstract][BibTex][pdf] Large portion sizes and a high eating rate are associated with high energy intake and obesity. Most individuals maintain their food intake weight (g) and eating rate (g/min) rank in relation to their peers, despite food and environmental manipulations. Single meal measures may enable identification of “large portion eaters” and “fast eaters,” finding individuals at risk of developing obesity. The aim of this study was to predict real-life food intake weight and eating rate based on one school lunch. Twenty-four high-school students with a mean (±SD) age of 16.8 yr (±0.7) and body mass index of 21.9 (±4.1) were recruited, using no exclusion criteria. Food intake weight and eating rate was first self-rated (“Less,” “Average” or “More than peers”), then objectively recorded during one school lunch (absolute weight of consumed food in grams). Afterwards, subjects recorded as many main meals (breakfasts, lunches and dinners) as possible in real-life for a period of at least two weeks, using a Bluetooth connected weight scale and a smartphone application. On average participants recorded 18.9 (7.3) meals during the study. Real-life food intake weight was 327.4 g (±110.6), which was significantly lower (p = 0.027) than the single school lunch, at 367.4 g (±167.2). When the intra-class correlation of food weight intake between the objectively recorded real-life and school lunch meals was compared, the correlation was excellent (R = 0.91). Real-life eating rate was 33.5 g/min (±14.8), which was significantly higher (p = 0.010) than the single school lunch, at 27.7 g/min (±13.3). The intra-class correlation of the recorded eating rate between real-life and school lunch meals was very large (R = 0.74). The participants’ recorded food intake weights and eating rates were divided into terciles and compared between school lunches and real-life, with moderate or higher agreement (? = 0.75 and ? = 0.54, respectively). In contrast, almost no agreement was observed between self-rated and real-life recorded rankings of food intake weight and eating rate (? = 0.09 and ? = 0.08, respectively). The current study provides evidence that both food intake weight and eating rates per meal vary considerably in real-life per individual. However, based on these behaviours, most students can be correctly classified in regard to their peers based on single school lunches. In contrast, self-reported food intake weight and eating rate are poor predictors of real-life measures. Finally, based on the recorded individual variability of real-life food intake weight and eating rate, it is not advised to rank individuals based on single recordings collected in real-life settings @article{Langlet2019Predicting, |
2019
| (C) | A. Papadopoulos, K. Kyritsis, S. Bostanjopoulou, L. Klingelhoefer, R. K. Chaudhuri and A. Delopoulos
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 Jul
[Abstract][BibTex][pdf] Parkinson’s Disease (PD) is a neurodegenerative disorder that manifests through slowly progressing symptoms, such as tremor, voice degradation and bradykinesia. Automated detection of such symptoms has recently received much attention by the research community, owing to the clinical benefits associated with the early diagnosis of the disease. Unfortunately, most of the approaches proposed so far, operate under a strictly laboratory setting, thus limiting their potential applicability in real world conditions. In this work, we present a method for automatically detecting tremorous episodes related to PD, based on acceleration signals. We propose to address the problem at hand, as a case of Multiple-Instance Learning, wherein a subject is represented as an unordered bag of signal segments and a single, expert-provided, ground-truth. We employ a deep learning approach that combines feature learning and a learnable pooling stage and is trainable end-to-end. Results on a newly introduced dataset of accelerometer signals collected in-the-wild confirm the validity of the proposed approach. @conference{alpapado2019embc, |
| (C) | Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos
41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Berlin, Germany, 2019 Jul
[Abstract][BibTex][pdf] Automated and objective monitoring of eating behavior has received the attention of both the research community and the industry over the past few years. In this paper we present a method for automatically detecting meals in free living conditions, using the inertial data (acceleration and orientation velocity) from commercially available smartwatches. The proposed method operates in two steps. In the first step we process the raw inertial signals using an End-to- End Neural Network with the purpose of detecting the bite events throughout the recording. During the next step, we process the resulting bite detections using signal processing algorithms to obtain the final meal start and end timestamp estimates. Evaluation results obtained from our Leave One Subject Out experiments using our publicly available FIC and FreeFIC datasets, exhibit encouraging results by achieving an F1/Average Jaccard Index of 0.894/0.804. @conference{kyritsis2019detecting, |
| (C) | Ioannis Sarafis, Christos Diou, Ioannis Ioakimidis and Anastasios Delopoulos
41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 Jul
[Abstract][BibTex][pdf] Certain patterns of eating behaviour during meal have been identified as risk factors for long-term abnormal eating development in healthy individuals and, eventually, can affect the body weight. To detect early signs of problematic eating behaviour, this paper proposes a novel method for building behaviour assessment models. The goal of the models is to predict whether the in-meal eating behaviour resembles patterns associated with obesity, eating disorders, or low-risk behaviours. The models are trained using meals recorded with a plate scale from a reference population and labels annotated by a domain expert. In addition, the domain expert assigned scores that characterise the degree of any exhibited abnormal patterns. To improve model effectiveness, we use the domain expert’s scores to create training error regularisation weights that alter the importance of each training instance for its class during model training. The behaviour assessment models are based on the SVM algorithm and the fuzzy SVM algorithm for their instance-weighted variation. Experiments conducted on meals recorded from 120 individuals show that: (a) the proposed approach can produce effective models for eating behaviour classification (for individuals), or for ranking (for populations); and (b) the instance-weighted fuzzy SVM models achieve significant performance improvements, compared to the non-weighted, standard SVM models. @conference{sarafis2019assessment, |
| (C) | Ioannis Sarafis, Christos Diou and Anastasios Delopoulos
41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 Jul
[Abstract][BibTex][pdf] Obesity is a preventable disease that affects the health of a significant population percentage, reduces the life expectancy and encumbers the health care systems. The obesity epidemic is not caused by isolated factors, but it is the result of multiple behavioural patterns and complex interactions with the living environment. Therefore, in-depth understanding of the population behaviour is essential in order to create successful policies against obesity prevalence. To this end, the BigO system facilitates the collection, processing and modelling of behavioural data at population level to provide evidence for effective policy and interventions design. In this paper, we introduce the behaviour profiles mechanism of BigO that produces comprehensive models for the behavioural patterns of individuals, while maintaining high levels of privacy protection. We give examples for the proposed mechanism from real world data and we discuss usages for supporting various types of evidence-based policy design. @conference{sarafis2019behaviour, |
2018
| (J) | Janet van den Boer, Annemiek van der Lee, Lingchuan Zhou, Vasileios Papapanagiotou, Christos Diou, Anastasios Delopoulos and Monica Mars
The SPLENDID Eating Detection Sensor: Development and Feasibility Study, 6, (9), pp. 170, 2018 Sep
[Abstract][BibTex] The available methods for monitoring food intake---which for a great part rely on self-report---often provide biased and incomplete data. Currently, no good technological solutions are available. Hence, the SPLENDID eating detection sensor (an ear-worn device with an air microphone and a photoplethysmogram [PPG] sensor) was developed to enable complete and objective measurements of eating events. The technical performance of this device has been described before. To date, literature is lacking a description of how such a device is perceived and experienced by potential users. Objective: The objective of our study was to explore how potential users perceive and experience the SPLENDID eating detection sensor. Methods: Potential users evaluated the eating detection sensor at different stages of its development: (1) At the start, 12 health professionals (eg, dieticians, personal trainers) were interviewed and a focus group was held with 5 potential end users to find out their thoughts on the concept of the eating detection sensor. (2) Then, preliminary prototypes of the eating detection sensor were tested in a laboratory setting where 23 young adults reported their experiences. (3) Next, the first wearable version of the eating detection sensor was tested in a semicontrolled study where 22 young, overweight adults used the sensor on 2 separate days (from lunch till dinner) and reported their experiences. (4) The final version of the sensor was tested in a 4-week feasibility study by 20 young, overweight adults who reported their experiences. Results: Throughout all the development stages, most individuals were enthusiastic about the eating detection sensor. However, it was stressed multiple times that it was critical that the device be discreet and comfortable to wear for a longer period. In the final study, the eating detection sensor received an average grade of 3.7 for wearer comfort on a scale of 1 to 10. Moreover, experienced discomfort was the main reason for wearing the eating detection sensor <2 hours a day. The participants reported having used the eating detection sensor on 19/28 instructed days on average. Conclusions: The SPLENDID eating detection sensor, which uses an air microphone and a PPG sensor, is a promising new device that can facilitate the collection of reliable food intake data, as shown by its technical potential. Potential users are enthusiastic, but to be successful wearer comfort and discreetness of the device need to be improved. @article{2018Boer, |
| (J) | Christos Diou, Pantelis Lelekas and Anastasios Delopoulos
Journal of Imaging, 4, (11), pp. 125, 2018 Oct
[Abstract][BibTex] Background: Evidence-based policymaking requires data about the local population’s socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however, these methods are also resource-intensive, since they require large volumes of manually labeled training data. (2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification, and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. (3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has R2=0.76 and a correlation coefficient of 0.874 with the true unemployment rate, while it achieves a mean absolute percentage error of 0.089 and mean absolute error of 1.87 on a held-out test set. Similar results are also obtained for other socioeconomic indicators, related to education level and occupational prestige. (4) Conclusions: The proposed methodology can be used to estimate SES indicators at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort @article{Diou2018JI, |
| (J) | Maryam Esfandiari, Vasilis Papapanagiotou, Christos Diou, Modjtaba Zandian, Jenny Nolstam, Per Södersten and Cecilia Bergh
JoVE, (135), 2018 May
[Abstract][BibTex] Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve of food intake, meal duration and rate of eating modeled by a quadratic equation. The purpose of the method is to change eating behavior by providing visual feedback on the computer screen that the subject can adapt to because her/his own rate of eating appears on the screen during the meal. The data generated by the method is automatically analyzed and fitted to the quadratic equation using a custom made algorithm. The method has the advantage of recording eating behavior objectively and offers the possibility of changing eating behavior both in experiments and in clinical practice. A limitation may be that experimental subjects are affected by the method. The same limitation may be an advantage in clinical practice, as eating behavior is more easily stabilized by the method. A treatment that uses this method has normalized body weight and restored the health of several hundred patients with anorexia nervosa and other eating disorders and has reduced the weight and improved the health of severely overweight patients. @article{Esfandiari2018, |
| (J) | George Mamalakis, Christos Diou, Andreas Symeonidis and Leonidas Georgiadis
Neural Computing and Applications, 2018 Jul
[Abstract][BibTex] In this work, we propose a methodology for reducing false alarms in file system intrusion detection systems, by taking into account the daemon's file system footprint. More specifically, we experimentally show that sequences of outliers can serve as a distinguishing characteristic between true and false positives, and we show how analysing sequences of outliers can lead to lower false positive rates, while maintaining high detection rates. Based on this analysis, we developed an anomaly detection filter that learns outlier sequences using k-nearest neighbours with normalised longest common subsequence. Outlier sequences are then used as a filter to reduce false positives on the {\$}{\$}FI^2DS{\$}{\$}FI2DSfile system intrusion detection system. This filter is evaluated on both overlapping and non-overlapping sequences of outliers. In both cases, experiments performed on three real-world web servers and a honeynet show that our approach achieves significant false positive reduction rates (up to 50 times), without any degradation of the corresponding true positive detection rates. @article{Mamalakis2018, |
| (J) | Ioannis Sarafis, Christos Diou and Anastasios Delopoulos
CoRR, abs/1809.06124, 2018 Sep
[Abstract][BibTex][pdf] Weighted SVM (or fuzzy SVM) is the most widely used SVM variant owning its effectiveness to the use of instance weights. Proper selection of the instance weights can lead to increased generalization performance. In this work, we extend the span error bound theory to weighted SVM and we introduce effective hyperparameter selection methods for the weighted SVM algorithm. The significance of the presented work is that enables the application of span bound and span-rule with weighted SVM. The span bound is an upper bound of the leave-one-out error that can be calculated using a single trained SVM model. This is important since leave-one-out error is an almost unbiased estimator of the test error. Similarly, the span-rule gives the actual value of the leave-one-out error. Thus, one can apply span bound and span-rule as computationally lightweight alternatives of leave-one-out procedure for hyperparameter selection. The main theoretical contributions are: (a) we prove the necessary and sufficient condition for the existence of the span of a support vector in weighted SVM; and (b) we prove the extension of span bound and span-rule to weighted SVM. We experimentally evaluate the span bound and the span-rule for hyperparameter selection and we compare them with other methods that are applicable to weighted SVM: the K-fold cross-validation and the $\xi - \alpha$ bound. Experiments on 14 benchmark data sets and data sets with importance scores for the training instances show that: (a) the condition for the existence of span in weighted SVM is satisfied almost always; (b) the span-rule is the most effective method for weighted SVM hyperparameter selection; (c) the span-rule is the best predictor of the test error in the mean square error sense; and (d) the span-rule is efficient and, for certain problems, it can be calculated faster than K-fold cross-validation. @article{Sarafis2018CoRR, |
| (J) | Vasilis Papapanagiotou, Christos Diou, Ioannis Ioakimidis, Per Sodersten and Anastasios Delopoulos
IEEE Journal of Biomedical and Health Informatics, PP, (99), pp. 1-1, 2018 Mar
[Abstract][BibTex][pdf] The structure of the cumulative food intake (CFI) curve has been associated with obesity and eating disorders. Scales that record the weight loss of a plate from which a subject eats food are used for capturing this curve; however, their measurements are contaminated by additive noise and are distorted by certain types of artifacts. This paper presents an algorithm for automatically processing continuous in-meal weight measurements in order to extract the clean CFI curve and in-meal eating indicators, such as total food intake and food intake rate. The algorithm relies on the representation of the weight-time series by a string of symbols that correspond to events such as bites or food additions. A context-free grammar is next used to model a meal as a sequence of such events. The selection of the most likely parse tree is finally used to determine the predicted eating sequence. The algorithm is evaluated on a dataset of 113 meals collected using the Mandometer, a scale that continuously samples plate weight during eating. We evaluate the effectiveness for seven indicators, and for bite-instance detection. We compare our approach with three state-of-the-art algorithms, and achieve the lowest error rates for most indicators (24 g for total meal weight). The proposed algorithm extracts the parameters of the CFI curve automatically, eliminating the need for manual data processing, and thus facilitating large-scale studies of eating behavior. @article{Vassilis2018, |
2018
| (M) | Christos Diou, Ioannis Ioakeimidis, Evangelia Charmandari, Penio Kassaric, Irini Lekka, Monica Mars, Cecilia Bergh, Tahar Kechadi, Gerardine Doyle, Grace O’Malley, Rachel Heimeier, Anna Karin Lindroos, Sofoklis Sotiriou, Evangelia Koukoula, Sergio Guillén, George Lymperopoulos, Nicos Maglaveras and Anastasios Delopoulos
Athens, Greece, 2018 Sep
[Abstract][BibTex] Background: Childhood obesity is a major global and European public health problem. The need for community-targeted actions has long been recognized, however it has been prevented by the lack of monitoring and evaluation framework, and the methodological inability to objectively quantify the local community characteristics in a reasonable timeframe. Recent technological achievements in mobile and wearable electronics and Big Data infrastructures allow the engagement of European citizens in the data collection process. @misc{Diou2018ESPE, |
2018
| (C) | Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Honolulu, HI, USA, 2018 Oct
[Abstract][BibTex][pdf] In this paper, we propose an end-to-end neural network (NN) architecture for detecting in-meal eating events (i.e., bites), using only a commercially available smartwatch. Our method combines convolutional and recurrent networks and is able to simultaneously learn intermediate data representations related to hand movements, as well as sequences of these movements that appear during eating. A promising F-score of 0.884 is achieved for detecting bites on a publicly available dataset with 10 subjects. @conference{Kiritsis2018, |
| (C) | Alexandros Papadopoulos, Konstantinos Kyritsis, Ioannis Sarafis and Anastasios Delopoulos
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Honolulu, HI, USA, 2018 Oct
[Abstract][BibTex][pdf] Automated monitoring and analysis of eating behaviour patterns, i.e., “how one eats”, has recently received much attention by the research community, owing to the association of eating patterns with health-related problems and especially obesity and its comorbidities. In this work, we introduce an improved method for meal micro-structure analysis. Stepping on a previous methodology of ours that combines feature extraction, SVM micro-movement classification and LSTM sequence modelling, we propose a method to adapt a pretrained IMU-based food intake cycle detection model to a new subject, with the purpose of improving model performance for that subject. We split model training into two stages. First, the model is trained using standard supervised learning techniques. Then, an adaptation step is performed, where the model is fine-tuned on unlabeled samples of the target subject via semisupervised learning. Evaluation is performed on a publicly available dataset that was originally created and used in [1] and has been extended here to demonstrate the effect of the semisupervised approach, where the proposed method improves over the baseline method. @conference{papadopoulos2018personalised, |
2017
| (J) | Billy Langlet, Anna Anvret, Christos Maramis, Ioannis Moulos, Vasileios Papapanagiotou, Christos Diou, Eirini Lekka, Rachel Heimeier, Anastasios Delopoulos and Ioannis Ioakimidis
Behaviour & Information Technology, 36, (10), pp. 1005-1013, 2017 May
[Abstract][BibTex][pdf] Studying eating behaviours is important in the fields of eating disorders and obesity. However, the current methodologies of quantifying eating behaviour in a real-life setting are lacking, either in reliability (e.g. self-reports) or in scalability. In this descriptive study, we deployed previously evaluated laboratory-based methodologies in a Swedish high school, using the Mandometer®, together with video cameras and a dedicated mobile app in order to record eating behaviours in a sample of 41 students, 16–17 years old. Without disturbing the normal school life, we achieved a 97% data-retention rate, using methods fully accepted by the target population. The overall eating style of the students was similar across genders, with male students eating more than females, during lunches of similar lengths. While both groups took similar number of bites, males took larger bites across the meal. Interestingly, the recorded school lunches were as long as lunches recorded in a laboratory setting, which is characterised by the absence of social interactions and direct access to additional food. In conclusion, a larger scale use of our methods is feasible, but more hypotheses-based studies are needed to fully describe and evaluate the interactions between the school environment and the recorded eating behaviours. @article{Langlet2017, |
2017
| (C) | Vasilis Papapanagiotou, Christos Diou, Lingjuan Zhou, Janet van den Boer, Monica Mars and Anastasios Delopoulos
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 817-820, IEEE, 2017 Jul
[Abstract][BibTex][pdf] Monitoring of eating behavior using wearable technology is receiving increased attention, driven by the recent advances in wearable devices and mobile phones. One particularly interesting aspect of eating behavior is the monitoring of chewing activity and eating occurrences. There are several chewing sensor types and chewing detection algorithms proposed in the bibliography, however no datasets are publicly available to facilitate evaluation and further research. In this paper, we present a multi-modal dataset of over 60 hours of recordings from 14 participants in semi-free living conditions, collected in the context of the SPLENDID project. The dataset includes raw signals from a photoplethysmography (PPG) sensor and a 3D accelerometer, and a set of extracted features from audio recordings; detailed annotations and ground truth are also provided both at eating event level and at individual chew level. We also provide a baseline evaluation method, and introduce the “challenge” of improving the baseline chewing detection algorithms. The dataset can be downloaded from http: //dx.doi.org/10.17026/dans-zxw-v8gy, and supplementary code can be downloaded from https://github. com/mug-auth/chewing-detection-challenge.git. @inproceedings{8036949, |
| (C) | Vasilis Papapanagiotou, Christos Diou and Anastasios Delopoulos
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1258-1261, 2017 Jul
[Abstract][BibTex][pdf] Detecting chewing sounds from a microphone placed inside the outer ear for eating behaviour monitoring still remains a challenging task. This is mainly due the difficulty in discriminating non-chewing sounds (e.g. speech or sounds caused by walking) from chews, as well as due to to the high variability of the chewing sounds of different food types. Most approaches rely on detecting distictive structures on the sound wave, or on extracting a set of features and using a classifier to detect chews. In this work, we propose to use feature-learning in the time domain with 1-dimensional convolutional neural networks for for chewing detection. We apply a network of convolutional layers followed by fully connected layers directly on windows of the audio samples to detect chewing activity, and then aggregate individual chews to eating events. Experimental results on a large, semi-free living dataset collected in the context of the SPLENDID project indicate high effectiveness, with an accuracy of 0.980 and F1 score of 0.883. @inproceedings{8037060, |
| (C) | Konstantinos Kyritsis, Christina L. Tatli, Christos Diou and Aanastasios Delopoulos
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2843-2846, IEEE, Seogwipo, South Korea, 2017 Jul
[Abstract][BibTex][pdf] Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls. Evaluation is carried out on an annotated dataset of 8 subjects where the proposed method achieves 0:78 precision and 0:77 recall. The evaluation dataset is publicly available at http://mug.ee.auth.gr/intake-cycle-detection/. @inproceedings{8037449, |
| (C) | Christos Diou, Ioannis Sarafis, Ioannis Ioakimidis and Anastasios Delopoulos
"Data-driven assessments for sensor measurements of eating behavior"
Biomedical & Health Informatics (BHI), 2017 IEEE EMBS International Conference on, pp. 129-132, 2017 Jan
[Abstract][BibTex][pdf] Two major challenges in sensor-based measurement and assessment of healthy eating behavior are (a) choosing the behavioral indicators to be measured, and (b) interpreting the measured values. While much of the work towards solving these problems belongs in the domain of behavioral science, there are several areas where technology can help. This paper outlines an approach for representing and interpreting eating and activity behavior based on sensor measurements and data available from a reference population. The main idea is to assess the “similarity” of an individual\'s behavior to previous data recordings of a relevant reference population. Thus, by appropriate selection of the indicators and reference data it is possible to perform comparative behavioral evaluation and support decisions, even in cases where no clear medical guidelines for the indicator values exist. We examine the simple, univariate case (one indicator) and then extend these ideas to the multivariate problem (several indicators) using one-class SVM to measure the distance from the reference population. @inproceedings{diou2017data, |
| (C) | Iason Karakostas, Vasileios Papapanagiotou and Anastasios Delopoulos
New Trends in Image Analysis and Processing -- ICIAP 2017, pp. 403-410, Springer International Publishing, Cham, 2017 Dec
[Abstract][BibTex] Monitoring of eating activity is a well-established yet challenging problem. Various sensors have been proposed in the literature, including in-ear microphones, strain sensors, and photoplethysmography. Most of these approaches use detection algorithms that include machine learning; however, a universal, non user-specific model is usually trained from an available dataset for the final system. In this paper, we present a chewing detection system that can adapt to each user independently using active learning (AL) with minimal intrusiveness. The system captures audio from a commercial bone-conduction microphone connected to an Android smart-phone. We employ a state-of-the-art feature extraction algorithm and extend the Support Vector Machine (SVM) classification stage using AL. The effectiveness of the adaptable classification model can quickly converge to that achieved when using the entire available training set. We further use AL to create SVM models with a small number of support vectors, thus reducing the computational requirements, without significantly sacrificing effectiveness. To support our arguments, we have recorded a dataset from eight participants, each performing once or twice a standard protocol that includes consuming various types of food, as well as non-eating activities such as silent and noisy environments and conversation. Results show accuracy of 0.85 and F1 score of 0.83 in the best case for the user-specific models. @inproceedings{Karakostas2017, |
| (C) | Angelos Katharopoulos, Despoina Paschalidou, Christos Diou and Anastasios Delopoulos
"Learning local feature aggregation functions with backpropagation"
25th European Signal Processing Conference (EUSIPCO), pp. 748-752, IEEE, Kos, Greece, 2017 Aug
[Abstract][BibTex][pdf] This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem). To achieve that, we compose the local feature aggregation function with the classifier cost function and we backpropagate the gradient of this cost function in order to update the local feature aggregation function parameters. Experiments on synthetic datasets indicate that our method discovers parameters that model the class-relevant information in addition to the local feature space. Further experiments on a variety of motion and visual descriptors, both on image and video datasets, show that our method outperforms other state-of-the-art local feature aggregation functions, such as Bag of Words, Fisher Vectors and VLAD, by a large margin. @inproceedings{Katharopoulos2017, |
| (C) | Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos
New Trends in Image Analysis and Processing -- ICIAP 2017: ICIAP International Workshops, pp. 411-418, Springer International Publishing, Catania, Italy, 2017 Sep
[Abstract][BibTex][pdf] Unobtrusive analysis of eating behavior based on Inertial Measurement Unit (IMU) sensors (e.g. accelerometer) is a topic that has attracted the interest of both the industry and the research community over the past years. This work presents a method for detecting food intake moments that occur during a meal session using the accelerometer and gyroscope signals of an off-the-shelf smartwatch. We propose a two step approach. First, we model the hand micro-movements that take place while eating using an array of binary Support Vector Machines (SVMs); then the detection of intake moments is achieved by processing the sequence of SVM score vectors by a Long Short Term Memory (LSTM) network. Evaluation is performed on a publicly available dataset with 10 subjects, where the proposed method outperforms similar approaches by achieving an F1 score of 0.892. @inproceedings{Kyritsis2017ICIAP, |
2016
| (J) | Vasilis Papapanagiotou, Christos Diou, Lingchuan Zhou, Janet van den Boer, Monica Mars and Anastasios Delopoulos
IEEE Journal of Biomedical and Health Informatics, PP, (99), pp. 1-1, 2016 Jan
[Abstract][BibTex][pdf] In the context of dietary management, accurate monitoring of eating habits is receiving increased attention. Wearable sensors, combined with the connectivity and processing of modern smart phones, can be used to robustly extract objective, and real-time measurements of human behaviour. In particular, for the task of chewing detection, several approaches based on an in-ear microphone can be found in the literature, while other types of sensors have also been reported, such as strain sensors. In this work, performed in the context of the SPLENDID project, we propose to combine an in-ear microphone with a photoplethysmography (PPG) sensor placed in the ear concha, in a new high accuracy and low sampling rate prototype chewing detection system. We propose a pipeline that initially processes each sensor signal separately, and then fuses both to perform the final detection. Features are extracted from each modality, and support vector machine (SVM) classifiers are used separately to perform snacking detection. Finally, we combine the SVM scores from both signals in a late-fusion scheme, which leads to increased eating detection accuracy. We evaluate the proposed eating monitoring system on a challenging, semi-free living dataset of 14 subjects, that includes more than 60 hours of audio and PPG signal recordings. Results show that fusing the audio and PPG signals significantly improves the effectiveness of eating event detection, achieving accuracy up to 0.938 and class-weighted accuracy up to 0.892. @article{7736096, |
| (J) | Antonios Chrysopoulos, Christos Diou, Andreas L. Symeonidis and Pericles A. Mitkas
"Response modeling of small-scale energy consumers for effective demand response applications"
Electric Power Systems Research, 132, pp. 78-93, 2016 Mar
[Abstract][BibTex][pdf] Abstract The Smart Grid paradigm can be economically and socially sustainable by engaging potential consumers through understanding, trust and clear tangible benefits. Interested consumers may assume a more active role in the energy market by claiming new energy products/services on offer and changing their consumption behavior. To this end, suppliers, aggregators and Distribution System Operators can provide monetary incentives for customer behavioral change through demand response programs, which are variable pricing schemes aiming at consumption shifting and/or reduction. However, forecasting the effect of such programs on power demand requires accurate models that can efficiently describe and predict changes in consumer activities as a response to pricing alterations. Current work proposes such a detailed bottom-up response modeling methodology, as a first step towards understanding and formulating consumer response. We build upon previous work on small-scale consumer activity modeling and provide a novel approach for describing and predicting consumer response at the level of individual activities. The proposed models are used to predict shifting of demand as a result of modified pricing policies and they incorporate consumer preferences and comfort through sensitivity factors. Experiments indicate the effectiveness of the proposed method on real-life data collected from two different pilot sites: 32 apartments of a multi-residential building in Sweden, as well as 11 shops in a large commercial center in Italy. @article{Chrysopoulos2016Response, |
| (J) | Vasileios Papapanagiotou, Christos Diou and Anastasios Delopoulos
ACM Transactions on Multimedia Computing, Communications, and Applications, 12, (2), 2016 Mar
[Abstract][BibTex][pdf] This article presents a novel approach to training classifiers for concept detection using tags and a variant of Support Vector Machine that enables the usage of training weights per sample. Combined with an appropriate tag weighting mechanism, more relevant samples play a more important role in the calibration of the final concept-detector model. We propose a complete, automated framework that (i) calculates relevance scores for each image-concept pair based on image tags, (ii) transforms the scores into relevance probabilities and automatically annotates each image according to this probability, (iii) transforms either the relevance scores or the probabilities into appropriate training weights and finally, (iv) incorporates the training weights and the visual features into a Fuzzy Support Vector Machine classifier to build the concept-detector model. The framework can be applied to online public collections, by gathering a large pool of diverse images, and using the calculated probability to select a training set and the associated training weights. To evaluate our argument, we experiment on two large annotated datasets. Experiments highlight the retrieval effectiveness of the proposed approach. Furthermore, experiments with various levels of annotation error show that using weights derived from tags significantly increases the robustness of the resulting concept detectors. @article{Papapanagiotou2016Improving, |
| (J) | Ioannis Sarafis, Christos Diou and Anastasios Delopoulos
"Online training of concept detectors for image retrieval using streaming clickthrough data"
Engineering Applications of Artificial Intelligence, 51, pp. 150-162, 2016 Jan
[Abstract][BibTex][pdf] Clickthrough data from image search engines provide a massive and continuously generated source of user feedback that can be used to model how the search engine users perceive the visual content. Image clickthrough data have been successfully used to build concept detectors without any manual annotation effort, although the generated annotations suffer from labeling errors. Previous research efforts therefore focused on modeling the sample uncertainty in order to improve concept detector effectiveness. In this paper, we study the problem in an online learning setting using streaming clickthrough data where each click is treated seperately when it becomes available; the concept detector model is therefore continuously updated without batch retraining. We argue that sample uncertainty can be incorporated in the online learning setting by exploiting the repetitions of incoming clicks at the classifier level, where these act as an implicit importance weighting mechanism. For online concept detector training we use the LASVM algorithm. The inferred weighting approximates the solution of batch trained concept detectors using weighted SVM variants that are known to achieve improved performance and high robustness to noise compared to the standard SVM. Furthermore, we evaluate methods for selecting negative samples using a small number of candidates sampled locally from the incoming stream of clicks. The selection criteria aim at drastically improving the performance and the convergence speed of the online concept detectors. To validate our arguments we conduct experiments for 30 concepts on the Clickture-Lite dataset. The experimental results demonstrate that: (a) the proposed online approach produces effective and noise resilient concept detectors that can take advantage of streaming clickthrough data and achieve performance that is equivalent to Fuzzy SVM concept detectors with sample weights and 78.6% improved compared to standard SVM concept detectors; and (b) the selection criteria speed up convergence and improve effectiveness compared to random negative sampling even for a small number of available clicks (up to 134% after 100 clicks). @article{Sarafis2016Online, |
2016
| (C) | Vasilis Papapanagiotou, Christos Diou, Lingchuan Zhou, Janet van den Boer, Monica Mars and Anastasios Delopoulos
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6485-6488, 2016 Aug
[Abstract][BibTex][pdf] Monitoring of human eating behaviour has been attracting interest over the last few years, as a means to a healthy lifestyle, but also due to its association with serious health conditions, such as eating disorders and obesity. Use of self-reports and other non-automated means of monitoring have been found to be unreliable, compared to the use of wearable sensors. Various modalities have been reported, such as acoustic signal from ear-worn microphones, or signal from wearable strain sensors. In this work, we introduce a new sensor for the task of chewing detection, based on a novel photoplethysmography (PPG) sensor placed on the outer earlobe to perform the task. We also present a processing pipeline that includes two chewing detection algorithms from literature and one new algorithm, to process the captured PPG signal, and present their effectiveness. Experiments are performed on an annotated dataset recorded from 21 individuals, including more than 10 hours of eating and non-eating activities. Results show that the PPG sensor can be successfully used to support dietary monitoring. @inproceedings{7592214, |
| (C) | Angelos Katharopoulos, Despoina Paschalidou, Christos Diou and Anastasios Delopoulos
"Fast Supervised LDA for discovering micro-events in large-scale video datasets"
In proceedings of the 24th ACM international conference on multimedia (ACM-MM 2016), Amsterdam, The Netherlands, 2016 Oct
[Abstract][BibTex][pdf] This paper introduces fsLDA, a fast variational inference method for supervised LDA, which overcomes the computational limitations of the original supervised LDA and enables its application in large-scale video datasets. In addition to its scalability, our method also overcomes the drawbacks of standard, unsupervised LDA for video, including its focus on dominant but often irrelevant video information (e.g. background, camera motion). As a result, experiments in the UCF11 and UCF101 datasets show that our method consistently outperforms unsupervised LDA in every metric. Furthermore, analysis shows that class-relevant topics of fsLDA lead to sparse video representations and encapsulate high-level information corresponding to parts of video events, which we denote \'\'micro-events\'\'. @inproceedings{KatharopoulosACMMM2016, |
2015
| (J) | Ioannis Sarafis, Christos Diou and Anastasios Delopoulos
"Building effective SVM concept detectors from clickthrough data for large-scale image retrieval"
International Journal of Multimedia Information Retrieval, 4, (2), pp. 129-142, 2015 Jun
[Abstract][BibTex][pdf] Clickthrough data is a source of information that can be used for automatically building concept detectors for image retrieval. Previous studies, however, have shown that in many cases the resulting training sets suffer from severe label noise that has a significant impact in the SVM concept detector performance. This paper evaluates and proposes a set of strategies for automatically building effective concept detectors from clickthrough data. These strategies focus on: (1) automatic training set generation; (2) assignment of label confidence weights to the training samples and (3) using these weights at the classifier level to improve concept detector effectiveness. For training set selection and in order to assign weights to individual training samples three Information Retrieval (IR) models are examined: vector space models, BM25 and language models. Three SVM variants that take into account importance at the classifier level are evaluated and compared to the standard SVM: the Fuzzy SVM, the Power SVM, and the Bilateral-weighted Fuzzy SVM. Experiments conducted on the MM Grand Challenge dataset (consisting of 1M images and 82.3M unique clicks) for 40 concepts demonstrate that (1) on average, all weighted SVM variants are more effective than the standard SVM; (2) the vector space model produces the best training sets and best weights; (3) the Bilateral-weighted Fuzzy SVM produces the best results but is very sensitive to weight assignment and (4) the Fuzzy SVM is the most robust training approach for varying levels of label noise. @article{Sarafis2015Building, |
2015
| (C) | Vasileios Papapanagiotou, Christos Diou, Billy Langlet, Ioannis Ioakimidis and Anastasios Delopoulos
Bioinformatics and Biomedical Engineering: Third International Conference, IWBBIO 2015, Granada, Spain, April 15-17, 2015. Proceedings, Part II, pp. 35-46, Springer International Publishing, Cham, 2015 Jan
[Abstract][BibTex] Recent studies and clinical practice have shown that the extraction of detailed eating behaviour indicators is critical in identifying risk factors and/or treating obesity and eating disorders, such as anorexia and bulimia nervosa. A number of single meal analysis methods that have been successfully applied are based on the Mandometer, a weight scale that continuously measures the weight of food on a plate over the course of a meal. Experimental meal analysis is performed using the cumulative food intake curve, which is produced by the semi-automatic processing of the Mandometer weight measurements, in tandem with the video recordings of the eating session. Due to its complexity and the video recording dependence, this process is not suited to a clinical or a real-life setting. In this work, we evaluate a method for automating the extraction of an accurate food intake curve, corrected for food additions during the meal and artificial weight fluctuations, using only the raw Mandometer output. Since the method requires no manual corrections or external video recordings it is appropriate for clinical or free-living use. Three algorithms are presented based on rules, greedy decisioning and exhaustive search, as well as evaluation methods of the Mandometer measurements. Experiments on a set of 114 meals collected from both normal and disordered eaters in a clinical environment illustrate the effectiveness of the proposed approach. @inproceedings{Papapanagiotou2015Automated, |
| (C) | Vasileios Papapanagiotou , Christos Diou, Zhou Lingchuan, Janet van den Boer, Monica Mars and Anastasios Delopoulos
New Trends in Image Analysis and Processing--ICIAP 2015 Workshops, pp. 401-408, 2015 Apr
[Abstract][BibTex][pdf] In the battle against Obesity as well as Eating Disorders, non-intrusive dietary monitoring has been investigated by many researchers. For this purpose, one of the most promising modalities is the acoustic signal captured by a common microphone placed inside the outer ear canal. Various chewing detection algorithms for this type of signals exist in the literature. In this work, we perform a systematic analysis of the fractal nature of chewing sounds, and find that the Fractal Dimension is substantially different between chewing and talking. This holds even for severely down-sampled versions of the recordings. We derive chewing detectors based on the the fractal dimension of the recorded signals that can clearly discriminate chewing from non-chewing sounds. We experimentally evaluate snacking detection based on the proposed chewing detector, and we compare our approach against well known counterparts. Experimental results on a large dataset of 10 subjects and total recordings duration of more than 8 hours demonstrate the high effectiveness of our method. Furthermore, there exists indication that discrimination between different properties (such as crispness) is possible. @inproceedings{Papapanagiotou2015Fractal, |
| (C) | Vasileios Papapanagiotou, Christos Diou, Billy Langlet, Ioannis Ioakimidis and Anastasios Delopoulos
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7853-7856, IEEE, 2015 Aug
[Abstract][BibTex][pdf] Monitoring and modification of eating behaviour through continuous meal weight measurements has been successfully applied in clinical practice to treat obesity and eating disorders. For this purpose, the Mandometer, a plate scale, along with video recordings of subjects during the course of single meals, has been used to assist clinicians in measuring relevant food intake parameters. In this work, we present a novel algorithm for automatically constructing a subject\'s food intake curve using only the Mandometer weight measurements. This eliminates the need for direct clinical observation or video recordings, thus significantly reducing the manual effort required for analysis. The proposed algorithm aims at identifying specific meal related events (e.g. bites, food additions, artifacts), by applying an adaptive pre-processing stage using Delta coefficients, followed by event detection based on a parametric Probabilistic Context-Free Grammar on the derivative of the recorded sequence. Experimental results on a dataset of 114 meals from individuals suffering from obesity or eating disorders, as well as from individuals with normal BMI, demonstrate the effectiveness of the proposed approach. @inproceedings{Papapanagiotou2015Parametric, |
2014
| (J) | Niki Aifanti and Anastasios Delopoulos
"Linear subspaces for facial expression recognition"
Signal Processing: Image Communication, 29, (1), pp. 177-188, 2014 Jan
[Abstract][BibTex][pdf] This paper presents a method for the recognition of the six basic facial expressions in images or in image sequences using landmark points. The proposed technique relies on the observation that the vectors formed by the landmark point coordinates belong to a different manifold for each of the expressions. In addition experimental measurements validate the hypothesis that each of these manifolds can be decomposed to a small number of linear subspaces of very low dimension. This yields a parameterization of the manifolds that allows for computing the distance of a feature vector from each subspace and consequently from each one of the six manifolds. Two alternative classifiers are next proposed that use the corresponding distances as input: the first one is based on the minimum distance from the manifolds, while the second one uses \\{SVMs\\ that are trained with the vector of all distances from each subspace. The proposed technique is tested for two scenarios, the subject-independent and the subject-dependent one. Extensive experiments for each scenario have been performed on two publicly available datasets yielding very satisfactory expression recognition accuracy. @article{Aifanti2014Linear, |
| (J) | A. Chrysopoulos, C. Diou, A.L. Symeonidis and P.A. Mitkas
"Bottom-up modeling of small-scale energy consumers for effective Demand Response Applications"
Engineering Applications of Artificial Intelligence, 35, pp. 299-315, 2014 Sep
[Abstract][BibTex][pdf] In contemporary power systems, small-scale consumers account for up to 50% of a country?s total electrical energy consumption. Nevertheless, not much has been achieved towards eliminating the problems caused by their inelastic consumption habits, namely the peaks in their daily power demand and the inability of energy suppliers to perform short-term forecasting and/or long-term portfolio management. Typical approaches applied in large-scale consumers, like providing targeted incentives for behavioral change, cannot be employed in this case due to the lack of models for everyday habits, activities and consumption patterns, as well as the inability to model consumer response based on personal comfort. Current work aspires to tackle these issues; it introduces a set of small-scale consumer models that provide statistical descriptions of electrical consumption patterns, parameterized from the analysis of real-life consumption measurements. These models allow (i) bottom-up aggregation of appliance use up to the overall installation load, (ii) simulation of various energy efficiency scenarios that involve changes at appliance and/or activity level and (iii) the assessment of change in consumer habits, and therefore the power consumption, as a result of applying different pricing policies. Furthermore, an autonomous agent architecture is introduced that adopts the proposed consumer models to perform simulation and result analysis. The conducted experiments indicate that (i) the proposed approach leads to accurate prediction of small-scale consumption (in terms of energy consumption and consumption activities) and (ii) small shifts in appliance usage times are sufficient to achieve significant peak power reduction. @article{Chrysopoulos2014Bottom, |
| (J) | G. Mamalakis, C. Diou, A.L. Symeonidis and L. Georgiadis
"Of daemons and men: A file system approach towards intrusion detection"
Applied Soft Computing, 25, pp. 1-14, 2014 Dec
[Abstract][BibTex][pdf] We present \\\\{FI2DS\\\\ a file system, host based anomaly detection system that monitors Basic Security Module (BSM) audit records and determines whether a web server has been compromised by comparing monitored activity generated from the web server to a normal usage profile. Additionally, we propose a set of features extracted from file system specific \\\\{BSM\\\\ audit records, as well as an \\\\{IDS\\\\ that identifies attacks based on a decision engine that employs one-class classification using a moving window on incoming data. We have used two different machine learning algorithms, Support Vector Machines (SVMs) and Gaussian Mixture Models (GMMs) and our evaluation is performed on real-world datasets collected from three web servers and a honeynet. Results are very promising, since \\\\{FI2DS\\\\ detection rates range between 91% and 95.9% with corresponding false positive rates ranging between 8.1× 10?2 % and 9.3× 10?4 %. Comparison of \\\\{FI2DS\\\\ to another state-of-the-art filesystem-based IDS, FWRAP, indicates higher effectiveness of the proposed \\\\{IDS\\\\ in all three datasets. Within the context of this paper \\\\{FI2DS\\\\ is evaluated for the web daemon user; nevertheless, it can be directly extended to model any daemon-user for both intrusion detection and postmortem analysis. @article{Mamalakis2014Daemons, |
| (J) | Christos Papachristou and Anastasios Delopoulos
"A method for the evaluation of projective geometric consistency in weakly calibrated stereo with application to point matching"
Computer Vision and Image Understanding, 119, pp. 81-101, 2014 Feb
[Abstract][BibTex][pdf] We present a novel method that evaluates the geometric consistency of putative point matches in weakly calibrated settings, i.e. when the epipolar geometry but not the camera calibration is known, using only the point coordinates as information. The main idea behind our approach is the fact that each point correspondence in our data belongs to one of two classes (inliers/outlier). The classification of each point match relies on the histogram of a quantity representing the difference between cross ratios derived from a construction involving 6-tuples of point matches. Neither constraints nor scenario dependent parameters/thresholds are needed. Even for few candidate point matches the ensemble of 6-tuples containing each of them turns to provide statistically reliable histograms that prove to discriminate between inliers and outliers. In fact, in most cases a random sampling among this population is sufficient. Nevertheless, the accuracy of the method is positively correlated to its sampling density leading to an accuracy versus resulting computational complexity trade-off. Theoretical analysis and experiments are given that show the consistent performance of the proposed classification method when applied in inlier/outlier discrimination. The achieved accuracy is favourably evaluated against established methods that employ geometric only information, i.e. those relying on the Sampson, the algebraic and the symmetric epipolar distances. Finally, we also present an application of our scheme in uncalibrated stereo inside a \\\\{RANSAC\\\\ framework and compare it to the same as above methods. @article{Papachristou2014Method, |
2014
| (C) | Christos Maramis, Christos Diou, Ioannis Ioakeimidis, Irini Lekka, Gabriela Dudnik, Monica Mars, Nikos Maglaveras, Cecilia Bergh and Anastasios Delopoulos
"SPLENDID: Preventing Obesity and Eating Disorders through Long-term Behavioural Modifications"
MOBIHEALTH 2014, ATHENES, Greece, 2014 Nov
[Abstract][BibTex] @inproceedings{Maramis2017SPLENDID, |
| (C) | Ioannis Sarafis, Christos Diou and Anastasios Delopoulos
"Building Robust Concept Detectors from Clickthrough Data: A Study in the MSR-Bing Dataset"
2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 66-71, 2014 Nov
[Abstract][BibTex][pdf] In this paper we extend our previous work on strategies for automatically constructing noise resilient SVM detectors from click through data for large scale concept-based image retrieval. First, search log data is used in conjunction with Information Retrieval (IR) models to score images with respect to each concept. The IR models evaluated in this work include Vector Space Models (VSM), BM25 and Language Models (LM). The scored images are then used to create training sets for SVM and appropriate sample weights for two SVM variants: the Fuzzy SVM (FSVM) and the Power SVM (PSVM). These SVM variants incorporate weights for each individual training sample and can therefore be used to model label uncertainty at the classifier level. Experiments on the MSR-Bing Image Retrieval Grand Challenge dataset (consisting of 1M images and 82.3M unique clicks) show that FSVM is the most robust SVM algorithm for handling label noise and that the highest performance is achieved with weights derived from VSM. These results extend our previous findings on the value of FSVM from professional image archives to large-scale general purpose search engines, and furthermore identify VSM as the most appropriate sample weighting model. @inproceedings{Sarafis2014Building, |
| (C) | Ioannis Sarafis, Christos Diou, Theodora Tsikrika and Anastasios Delopoulos
"Weighted SVM from clickthrough data for image retrieval"
2014 IEEE International Conference on Image Processing (ICIP), pp. 3013-3017, 2014 Aug
[Abstract][BibTex][pdf] In this paper we propose a novel approach to training noise-resilient concept detectors from clickthrough data collected by image search engines. We take advantage of the query logs to automatically produce concept detector training sets; these suffer though from label noise, i.e., erroneously assigned labels. We explore two alternative approaches for handling noisy training data at the classifier level by training concept detectors with two SVM variants: the Fuzzy SVM and the Power SVM. Experimental results on images collected from a professional image search engine indicate that 1) Fuzzy SVM outperforms both SVM and Power SVM and is the most effective approach towards handling label noise and 2) the performance gain of Fuzzy SVM compared to SVM increases progressively with the noise level in the training sets. @inproceedings{Sarafis2014Weighted, |
| (C) | Theodora Tsikrika and Christos Diou
"Multi-evidence User Group Discovery in Professional Image Search"
Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014., pp. 693-699, Springer International Publishing, Cham, 2014 Apr
[Abstract][BibTex][pdf] This work evaluates the combination of multiple evidence for discovering groups of users with similar interests. User groups are created by analysing the search logs recorded for a sample of 149 users of a professional image search engine in conjunction with the textual and visual features of the clicked images, and evaluated by exploiting their topical classification. The results indicate that the discovered user groups are meaningful and that combining textual and visual features improves the homogeneity of the user groups compared to each individual feature. @inproceedings{Tsikrika2014Multi, |
2013
| (J) | Nikolaos Dimitriou and Anastasios Delopoulos
"Motion-based segmentation of objects using overlapping temporal windows"
Image and Vision Computing, 31, (9), pp. 593-602, 2013 Sep
[Abstract][BibTex][pdf] Motion segmentation refers to the problem of separating the objects in a video sequence according to their motion. It is a fundamental problem of computer vision, since various systems focusing on the analysis of dynamic scenes include motion segmentation algorithms. In this paper we present a novel approach, where a video shot is temporally divided in successive and overlapping windows and motion segmentation is performed on each window respectively. This attribute renders the algorithm suitable even for long video sequences. In the last stage of the algorithm the segmentation results for every window are aggregated into a final segmentation. The presented algorithm can handle effectively asynchronous trajectories on each window even when they have no temporal intersection. The evaluation of the proposed algorithm on the Berkeley motion segmentation benchmark demonstrates its scalability and accuracy compared to the state of the art. @article{Dimitriou2013Motion, |
| (J) | Christos Maramis, Manolis Falelakis, Irini Lekka, Christos Diou, Pericles Mitkas and Anastasios Delopoulos
"Applying semantic technologies in cervical cancer research"
Data & Knowledge Engineering, 86, pp. 160-178, 2013 Jul
[Abstract][BibTex][pdf] In this paper we present a research system that follows a semantic approach to facilitate medical association studies in the area of cervical cancer. Our system, named \\{ASSIST\\ and developed as an \\{EU\\ research project, assists in cervical cancer research by unifying multiple patient record repositories, physically located in different medical centers or hospitals. Semantic modeling of medical data and rules for inferring domain-specific information allow the system to (i) homogenize the information contained in the isolated repositories by translating it into the terms of a unified semantic representation, (ii) extract diagnostic information not explicitly stored in the individual repositories, and (iii) automate the process of evaluating medical hypotheses by performing case–control association studies, which is the ultimate goal of the system. @article{Maramis2013Applying, |
2013
| (C) | Antonios Chrysopoulos, Christos Diou, Andreas L. Symeonidis and Pericles A. Mitkas
"Agent-Based Small-Scale Energy Consumer Models for Energy Portfolio Management"
International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM, pp. 94-101, IEEE, 2013 Nov
[Abstract][BibTex][pdf] In contemporary power systems, residential consumers may account for up to 50% of a country\'s total electrical energy consumption. Even though they constitute a significant portion of the energy market, not much has been achieved towards eliminating the inability for energy suppliers to perform long-term portfolio management, thus maximizing their revenue. The root cause of these problems is the difficulty in modeling consumers\' behavior, based on their everyday activities and personal comfort. If one were able to provide targeted incentives based on consumer profiles, the expected impact and market benefits would be significant. This paper introduces a formal residential consumer modeling methodology, that allows (i) the decomposition of the observed electrical load curves into consumer activities and, (ii) the evaluation of the impact of behavioral changes on the household\'s aggregate load curve. Analyzing electrical consumption measurements from DEHEMS research project enabled the model extraction of real-life consumers. Experiments indicate that the proposed methodology produces accurate small-scale consumer models and verify that small shifts in appliance usage times are sufficient to achieve significant peak power reduction. @inproceedings{Chrysopoulos2013Agent, |
2012
| (C) | Georgios T. Andreou, Andreas L. Symeonidis, Christos Diou, Pericles A. Mitkas and Dimitrios P. Labridis
"A framework for the implementation of large scale Demand Response"
2012 International Conference on Smart Grid Technology, Economics and Policies (SG-TEP), pp. 1-4, IEEE, 2012 Dec
[Abstract][BibTex][pdf] The rationalization of electrical energy consumption is a constant goal driving research over the last decades. The pursuit of efficient solutions requires the involvement of electrical energy consumers through Demand Response programs. In this study, a framework is presented that can serve as a tool for designing and simulating Demand Response programs, aiming at energy efficiency through consumer behavioral change. It provides the capability to dynamically model groups of electrical energy consumers with respect to their consumption, as well as their behavior. This framework is currently under development within the scope of the EU funded FP7 project “CASSANDRA - A multivariate platform for assessing the impact of strategic decisions in electrical power systems”. @inproceedings{Andreou2012Framework, |
2012
| (I) | Christos Maramis, Dimitrios Karagiannis and Anastasios Delopoulos
"HPVTyper: A Software Application for Automatic HPV Typing via PCR-RFLP Gel Electrophoresis"
Charpter:16, 29, pp. 01, InTech, 2012 Jan
[Abstract][BibTex][pdf] The quantitative information extraction from PCR-RFLP gel electrophoresis images requires the efficient modeling of the lane intensity profiles. To improve the acquired modeling accuracy, we introduce two novel ideas that can be incorporated in the modeling process. The first one proposes the use of the simplified integrated Weibull function as the basis function of the employed superposition model and the second proposes switching the domain of the intensity profile tobe-modeled to the unexploited fragment length domain. @inbook{Maramis2012HPVTyper, |
2011
| (J) | Christos Maramis, Anastasios Delopoulos and Alexandros Lambropoulos
"A Computerized Methodology for Improved Virus Typing by PCR-RFLP Gel Electrophoresis"
IEEE Transactions on Biomedical Engineering, 58, (8), pp. 2339-2351, 2011 Aug
[Abstract][BibTex][pdf] The analysis of digitized images from polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) gel electrophoresis examinations is a popular method for virus typing, i.e., for identifying the virus type(s) that have infected an investigated biological sample. However, being mostly manual, the conventional virus typing protocol remains laborious, time consuming, and error prone. In order to overcome these shortcomings, we propose a computerized methodology for improving virus typing via PCR-RFLP gel electrophoresis. A novel realistic observation model of the viral DNA motion on the gel matrix is employed to assist in exploiting additional virus-related information in comparison to the conventional approaches. The extracted rich information is fed to a novel typing algorithm, resulting in faster and more accurate decisions. The proposed methodology is evaluated for the case of the human papillomavirus typing on a dataset of 80 real and 1500 simulated samples, producing very satisfactory results. @article{Maramis2011Computerized, |
| (J) | Christos Maramis and Anastasios Delopoulos
"A Novel Algorithm for Restricting the Complexity of Virus Typing via PCR-RFLP Gel Electrophoresis"
Biomedical Engineering Letters, 1, (4), pp. 239-246, 2011 Nov
[Abstract][BibTex][pdf] PCR-RFLP gel electrophoresis is a popular method for virus typing (i.e., for identifying the types of a virus that have infected a biological sample), which has been automated recently owing to a computerized typing methodology. However, even with the help of this methodology, the PCRRFLP method suffers from low throughput, when compared to other typing methods. In this paper, we tackle this issue by introducing a novel algorithm for conducting the most computationally demanding phase of the aforementioned typing methodology (testing phase). @article{Maramis2011Novel, |
2011
| (C) | Christos F. Maramis, Anastasios N. Delopoulos, Alexandros F. Lambropoulos and Sokratis P. Katafigiotis
"A system for automatic HPV typing via PCR-RFLP gel electrophoresis"
2011 IEEE Conference on Automation Science and Engineering (CASE), pp. 549-556, IEEE, 2011 Aug
[Abstract][BibTex][pdf] The identification of the types of the human papillomavirus (HPV) that have infected a female patient provides valuable information as regards to her risk for developing cervical cancer. A widely used method for performing the above task (namely HPV typing) is PCR-RFLP gel electrophoresis. However, the conventional HPV typing protocol is error-prone and resource-ineffective due to lack of interaction between the phases involved in it. In order to treat these shortcomings, we introduce a novel HPV typing system that can be built upon widely available laboratory equipment. The proposed workflow of the system automates the task of HPV typing via PCRRFLP gel electrophoresis. The proof-of-concept of the proposed methodology is evaluated via an experiment that emulates the operation of the introduced system on a set of real HPV data. @inproceedings{Maramis2011System, |
2010
| (J) | Christos Diou, George Stephanopoulos, Panagiotis Panagiotopoulos, Christos Papachristou, Nikos Dimitriou and Anastasios Delopoulos
"Large-Scale Concept Detection in Multimedia Data Using Small Training Sets and Cross-Domain Concept Fusion"
IEEE Transactions on Circuits and Systems for Video Technology, 20, (12), pp. 1808 - 1821, 2010 Oct
[Abstract][BibTex][pdf] This paper presents the concept detector module developed for the VITALAS multimedia retrieval system. It outlines its architecture and major implementation aspects, including a set of procedures and tools that were used for the development of detectors for more than 500 concepts. The focus is on aspects that increase the system\'s scalability in terms of the number of concepts: collaborative concept definition and disambiguation, selection of small but sufficient training sets and efficient manual annotation. The proposed architecture uses cross-domain concept fusion to improve effectiveness and reduce the number of samples required for concept detector training. Two criteria are proposed for selecting the best predictors to use for fusion and their effectiveness is experimentally evaluated for 221 concepts on the TRECVID-2005 development set and 132 concepts on a set of images provided by the Belga news agency. In these experiments, cross-domain concept fusion performed better than early fusion for most concepts. Experiments with variable training set sizes also indicate that cross-domain concept fusion is more effective than early fusion when the training set size is small. @article{Diou2011Large, |
| (J) | Theodora Tsikrika, Christos Diou, Arjen P. de Vries and Anastasios Delopoulos
"Reliability and effectiveness of clickthrough data for automatic image annotation"
Multimedia Tools and Applications, 55, (1), pp. 27-52, 2010 Aug
[Abstract][BibTex][pdf] Automatic image annotation using supervised learning is performed by concept classifiers trained on labelled example images. This work proposes the use of clickthrough data collected from search logs as a source for the automatic generation of concept training data, thus avoiding the expensive manual annotation effort. We investigate and evaluate this approach using a collection of 97,628 photographic images. The results indicate that the contribution of search log based training data is positive despite their inherent noise; in particular, the combination of manual and automatically generated training data outperforms the use of manual data alone. It is therefore possible to use clickthrough data to perform large-scale image annotation with little manual annotation effort or, depending on performance, using only the automatically generated training data. An extensive presentation of the experimental results and the accompanying data can be accessed at http://olympus.ee.auth.gr/{\\textasciitildediou/civr2009/. @article{Tsikrika2011Reliability, |
2010
| (C) | Christos Diou, George Stephanopoulos and Anastasios Delopoulos
"The Multimedia Understanding Group at TRECVID-2010"
Proceedings of the TRECVID 2010 Workshop, 2010 Jan
[Abstract][BibTex][pdf] This is a report of the Multimedia Understanding Group participation in TRECVID-2010, where we submitted full runs for the Semantic Indexing (SIN) task. Our submission aims at experimentally evaluating three research items, that are important for work that is currently in progress. First, we examine the use of bag-of-words audio features for video concept detection, with noisy and/or low-quality video data. Although audio is important for some concepts and has shown promising results at other datasets, the results indicate that it can also lead to a decrease in performance when the quality is low and the negative examples are not adequately represented. We also explore the possibility of using a cross-domain concept fusion approach for reducing the number of dimensions at the final classifier. The corresponding experiments show, however, that when drastically reducing the number of dimensions the effectiveness drops. Finally, we also examined a transformation of the feature space, using a set of functions that are parametrically constructed from the data. @inproceedings{Diou2010Multimedia, |
| (C) | Christos F. Maramis, Anastasios N. Delopoulos and Alexandros F. Lambropoulos
"Analysis of PCR-RFLP Gel Electrophoresis Images for Accurate and Automated HPV Typing"
Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine, pp. 1-6, IEEE, 2010 Nov
[Abstract][BibTex][pdf] The identification of the types of the human papilomavirus (HPV) that have infected a woman provides valuable information as regards to her risk for developing cervical cancer. HPV typing is often performed by means of manually analyzing PCR-RFLP gel electrophoresis images. However, the typing procedure that is currently employed suffers from unsatisfactory accuracy and high time consumption. In order to treat these problems we propose a novel approach to HPV typing that automates the analysis of the electrophoretic images and concurrently improves the accuracy of the typing decision. The proposed methodology contributes both to the extraction of information from the images through a novel modeling approach and also to the process of making a typing decision based on the above information by the introduction of an original HPV typing algorithm. The efficiency of our approach is demonstrated with the help of a complex worked example that involves multiple HPV infections. @inproceedings{Maramis2010Analysis, |
| (C) | Christos Maramis, Evangelia Minga and Anastasios Delopoulos
"An Application for Semi-automatic HPV Typing of PCR-RFLP Images"
Image Analysis and Recognition: 7th International Conference, ICIAR 2010, P{\'ovoa de Varzin, Portugal, June 21-23, 2010, Proceedings, Part II, pp. 173-184, Springer Berlin Heidelberg, 2010 Jun
[Abstract][BibTex][pdf] The human papillomavirus, coming in over 100 flavors/types, is the causal factor of cervical cancer. The identification of the types that have infected the cervix of a patient is a very laborious yet critical task for molecular biologists that is still performed manually. HPV-Typer is a novel research software application that assists biologists by analyzing digitized images of electrophorized gel matrices that contain cervical samples processed by the PCR-RFLP technique in order to semi-automatically identify the existing types of the virus. HPVTyper has been designed to be functional under minimum user input conditions and yet to allow the user to intervene in any step of the typing procedure. @inproceedings{Maramis2010Application, |
| (C) | Christos Maramis and Anastasios Delopoulos
"Efficient Quantitative Information Extraction from PCR-RFLP Gel Electrophoresis Images"
2010 20th International Conference on Pattern Recognition (ICPR), pp. 2560 - 2563, IEEE, 2010 Aug
[Abstract][BibTex][pdf] For the purpose of PCR-RFLP analysis, as in the case of human papillomavirus (HPV) typing, quantitative information needs to be extracted from images resulting from one-dimensional gel electrophoresis by associating the image intensity with the concentration of biological material at the corresponding position on a gel matrix. However, the background intensity of the image stands in the way of quantifying this association. We propose a novel, efficient methodology for modeling the image background with a polynomial function and prove that this can benefit the extraction of accurate information from the lane intensity profile when modeled by a superposition of properly shaped parametric functions. @inproceedings{Maramis2010Efficient, |
2010
| (I) | Christos Maramis and Anastasios Delopoulos
"Improved Modeling of Lane Intensity Profiles on Gel Electrophoresis Images"
Charpter:4, 29, pp. 671-674, Springer Berlin Heidelberg, 2010 May
[Abstract][BibTex][pdf] The quantitative information extraction from PCR-RFLP gel electrophoresis images requires the efficient modeling of the lane intensity profiles. To improve the acquired modeling accuracy, we introduce two novel ideas that can be incorporated in the modeling process. The first one proposes the use of the simplified integrated Weibull function as the basis function of the employed superposition model and the second proposes switching the domain of the intensity profile tobe-modeled to the unexploited fragment length domain. @inbook{Maramis2010Improved, |
2009
| (J) | Theodoros Agorastos, Vassilis Koutkias, Manolis Falelakis, Irini Lekka, Themistoklis Mikos, Anastasios Delopoulos, Pericles Mitkas, Antonios Tantsis, Steven Weyers, Pascal Coorevits, Andreas Kaufmann, Roberto Kurzeja and Nicos Maglaveras
"Semantic Integration of Cervical Cancer Data Repositories to Facilitate Multicenter Association Studies: The ASSIST Approach"
Cancer Informatics, 8, pp. 31-31, 2009 Jan
[Abstract][BibTex][pdf] The current work addresses the unification of Electronic Health Records related to cervical cancer into a single medical knowledge source, in the context of the EU-funded ASSIST research project. The project aims to facilitate the research for cervical precancer and cancer through a system that virtually unifies multiple patient record repositories, physically located in different medical centers/hospitals, thus, increasing flexibility by allowing the formation of study groups \"on demand\" and by recycling patient records in new studies. To this end, ASSIST uses semantic technologies to translate all medical entities (such as patient examination results, history, habits, genetic profile) and represent them in a common form, encoded in the ASSIST Cervical Cancer Ontology. The current paper presents the knowledge elicitation approach followed, towards the definition and representation of the disease\'s medical concepts and rules that constitute the basis for the ASSIST Cervical Cancer Ontology. The proposed approach constitutes a paradigm for semantic integration of heterogeneous clinical data that may be applicable to other biomedical application domains. @article{Agorastos2009Semantic, |
2009
| (C) | Christos Diou, George Stephanopoulos, Nikos Dimitriou, Panagiotis Panagiotopoulos, Christos Papachristou, Anastasios Delopoulos, Henning Rode, Theodora Tsikrika, Arjen P. de Vries, Daniel Schneider, Jochen Schwenninger, Marie-Luce Viaud, Agnès Saulnier, Peter Altendorf, Birgit Schröter, Matthias Elser, Angel Rego, Alex Rodriguez, Cristina Martínez, Iñaki Etxaniz, Gérard Dupont, Bruno Grilhères, Nicolas Martin, Nozha Boujemaa, Alexis Joly, Raffi Enficiaud and Anne Verroust
"VITALAS at TRECVID 2009"
2009 TREC Video Retrieval Evaluation Workshop TRECVID-2009, 2009 Jan
[Abstract][BibTex][pdf] This paper describes the participation of VITALAS in the TRECVID-2009 evaluation where we submitted runs for the High-Level Feature Extraction (HLFE) and Interactive Search tasks.For the HLFE task, we focus on the evaluation of low-level feature sets and fusion methods. The runs employ multiple low-level features based on all available modalities (visual,audio and text) and the results show that use of such features improves the retrieval effectiveness significantly. We also use a concept score fusion approach that achieves good results with reduced low-level feature vector dimensionality. Furthermore, a weighting scheme is introduced for cluster assignment in the “bag-of-words” approach. Our runs achieved good performance compared to a baseline run and the submissions of other TRECVID-2009 participants. For the Interactive Search task, we focus on the evaluation of the integrated VITALAS system in order to gain insights into the use and effectiveness of the system’s search functionalities on (the combination of) multiple modalities and study the behavior of two user groups: professional archivists and non-professional users. Our analysis indicates that both user groups submit about the same total number of queries and use the search functionalities in a similar way, but professional users save twice as many shots and examine shots deeper in the ranked retrieved list.The agreement between the TRECVID assessors and our users was quite low. In terms of the effectiveness of the different search modalities, similarity searches retrieve on average twice as many relevant shots as keyword searches, fused searches three times as many, while concept searches retrieve even up to five times as many relevant shots, indicating the benefits of the use of robust concept detectors in multimodal video retrieval. @inproceedings{Diou2009VITALAS, |
| (C) | Manolis Falelakis, Lazaros Karydas and Anastasios Delopoulos
"Knowledge-Based Concept Score Fusion for Multimedia Retrieval"
WIC International Conference on Active Media Technologies, pp. 126-135, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009 Jan
[Abstract][BibTex][pdf] Automated detection of semantic concepts in multimedia documents has been attracting intensive research e?orts over the last years. These e?orts can be generally classi?ed in two categories of methodologies: the ones that attempt to solve the problem using discriminative methods (classi?ers) and those that build knowledge-based models, as driven by the W3C consortium. This paper proposes a methodology that tries to combine both approaches for multimedia retrieval. Our main contribution is the adoption of a formal model for de?ning concepts using logic and the incorporation of the output of concept classi?ers to the computation of annotation scores. Our method does not require the computationally intensive training of new classi?ers for the concepts de?ned. Instead, it employs a knowledge-based mechanism to combine the output score of existing classi?ers and can be used for ither detecting new concepts or enhancing the accuracy of existing detectors. Optimization procedures are employed to adapt the concept de?nitions to the multimedia corpus in hand, further improving the attained accuracy. Experiments using the TRECVID2005 video collection demonstrate promising results. @inproceedings{Falelakis2009Knowledge, |
| (C) | Manolis Falelakis, Christos Maramis, Irini Lekka, Pericles Mitkas and Anastasios Delopoulos
"An Ontology for Supporting Clincal Research on Cervical Cancer"
International Conference on Knowledge Engineering and Ontology Development, pp. 103-108, Madeira, Portugal, 2009 Jan
[Abstract][BibTex][pdf] This work presents an ontology for cervical cancer that is positioned in the center of a research system for conducting association studies. The ontology aims at providing a uni?ed ”language” for various heterogeneous medical repositories. To this end, it contains both generic patient-management and domain-speci?c concepts, as well as proper uni?cation rules. The inference scheme adopted is coupled with a procedural programming layer in order to comply with the design requirements. @inproceedings{Falelakis2009Ontology, |
| (C) | Theodora Tsikrika, Christos Diou, Arjen P. de Vries and Anastasios Delopoulos
"Are clickthrough data reliable as image annotations?"
Proceedings of the Theseus/ImageCLEF workshop on visual information retrieval evaluation, 2009 Sep
[Abstract][BibTex][pdf] We examine the reliability of clickthrough data as concept-based image annotations, by comparing them against manual annotations, for different concept categories. Our analysis shows that, for many concepts, the image annotations generated by using clickthrough data are reliable, with up to 90% of true positives in the automatically annotated images compared to the manual ground truth. Concept categories, though, do not provide additional evidence about the types of concepts for which clickthrough- based image annotation performs well. @inproceedings{Tsikrika2009Clickthrough, |
| (C) | Theodora Tsikrika, Christos Diou, Arjen P. de Vries and Anastasios Delopoulos
"Image Annotation Using Clickthrough Data"
Proceedings of the ACM International Conference on Image and Video Retrieval, ACM, New York, NY, USA, 2009 Jan
[Abstract][BibTex][pdf] Automatic image annotation using supervised learning is performed by concept classifiers trained on labelled example images. This work proposes the use of clickthrough data collected from search logs as a source for the automatic generation of concept training data, thus avoiding the expensive manual annotation effort. We investigate and evaluate this approach using a collection of 97,628 photographic images. The results indicate that the contribution of search log based training data is positive; in particular, the combination of manual and automatically generated training data outperforms the use of manual data alone. It is therefore possible to use clickthrough data to perform large-scale image annotation with little manual annotation effort or, depending on performance, using only the automatically generated training data. The datasets used as well as an extensive presentation of the experimental results can be accessed at http://olympus.ee.auth.gr/~diou/civr2009/. @inproceedings{Tsikrika2009Image, |
2008
| (J) | Manolis Falelakis, Christos Diou and Anastasios Delopoulos
"Complexity control in semantic identification"
International Journal of Intelligent Systems Technologies and Applications, 1, (3/4), pp. 247-262, 2008 Jan
[Abstract][BibTex][pdf] This work introduces an efficient scheme for identifying semantic entities within multimedia data sets, providing mechanisms for modelling the trade-off between the accuracy of the result and the entailed computational cost. Semantic entities are described through formal definitions based on lower-level semantic and/or syntactic features. Based on appropriate metrics, the paper presents a methodology for selecting optimal subsets of syntactic features to extract, so that satisfactory results are obtained, while complexity remains below some required limit. @article{Falelakis2006Complexity, |
2008
| (C) | Theodoros Agorastos, Pericles Mitkas, Manolis Falelakis, Fotis Psomopoulos, Anastasios Delopoulos, Andreas Symeonidis, Sotiris Diplaris, Christos Maramis, Alex andros Batzios, Irini Lekka, Vassilis Koutkias, Themistoklis Mikos, Antonios Tantsis and Nicos Maglaveras
"Large Scale Association Studies Using Unified Data for Cervical Cancer and beyond: The ASSIST Project"
World Cancer Congress, Geneva, Switzerland, 2008, 2008 Jan
[Abstract][BibTex] @inproceedings{Agorastos2008Large, |
| (C) | Christos Dimou, Manolis Falelakis, Andreas Symeonidis, Anastasios Delopoulos and Pericles Mitkas
"Constructing Optimal Fuzzy Metric Trees for Agent Performance Evaluation"
IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT2008), pp. 336-339, Sydney, Australia, 2008 Jan
[Abstract][BibTex][pdf] The field of multi-agent systems has reached a significant degree of maturity with respect to frameworks, standards and infrastructures. Focus is now shifted to performance evaluation of real-world applications, in order to quantify the practical benefits and drawbacks of agent systems. Our approach extends current work on generic evaluation methodologies for agents by employing fuzzy weighted trees for organizing evaluation-specific concepts/metrics and linguistic terms to intuitively represent and aggregate measurement information.Furthermore, we introduce meta-metrics that measure the validity and complexity of the contribution of each metric in the overall performance evaluation. These are all incorporated for selecting optimal subsets of metrics and designing the evaluation process incompliance with the demands/restrictions of various evaluation setups, thus minimizing intervention by domain experts. The applicability of the proposed methodology is demonstrated through the evaluation of a real-world test case. @inproceedings{Dimou2008Constructing, |
| (C) | Christos Diou, Christos Papachristou, Panagiotis Panagiotopoulos, George Stephanopoulos, Nikos Dimitriou, Anastasios Delopoulos, Henning Rode, Robin Aly, Arjen P. de Vries and Theodora Tsikrika
"VITALAS at TRECVID-2008"
6th TREC Video Retrieval Evaluation Workshop TRECVID08, Gaithersburg, USA, 2008 Jan
[Abstract][BibTex][pdf] This is the ?rst participation of VITALAS in TRECVID. In the high level feature extraction task, our submitted runs are based mainly on visual features, while one run utilizes audio information as well; the text is not used. The experiments performed aim at evaluating the e?ectiveness of different approaches to input processing prior to the ?nal classi?cation (i.e., ranking) stage. These are (i) clustering of feature vectors within the feature space, (ii) fusion of classi?er output scores for other concepts and (iii) feature selection. The results indicate that (i) fusion of the classi?er output of other concepts can provide valuable information, even if the original features are not discriminative, (ii) feature selection generally improves the results (especially when the original number of dimensions is high) and (iii) clustering within the feature space with small number of clusters does not seem to provide any signi?cant additional information. Our experiments for the search task are focused on concept retrieval. We generate an arti?cial text collection by merging context descriptions according to the probability of each concept to occur in a given shot. To make the approach feasible, we further need to investigate techniques for pruning the dense shot concept matrix. Despite the poor overall retrieval quality, our concept search runs show a similar performance to the pure ASR run. Only the combination of ASR and concept search yields considerable improvements. Among the tested concept pruning strategies, the simple top k selection works better than the deviationbased thresholding. @inproceedings{Diou2008VITALAS, |
| (C) | Pericles Mitkas, Christos Maramis, Anastasios Delopoulos, Andreas Symeonidis, Sotiris Diplaris, Manolis Falelakis, Fotis Psomopoulos, Alex andros Batzios, Nicos Maglaveras, Irini Lekka, Vassilis Koutkias, Theodoros Agorastos, Themistoklis Mikos and Antonios Tantsis
"ASSIST: Employing Inference and Semantic Technologies to Facilitate Association Studies on Cervical Cancer"
6th European Symposium on Biomedical Engineering, Chania, Crete, Greece, 2008 Jan
[Abstract][BibTex][pdf] Advances in biomedical engineering have lately facilitated medical data acquisition, leading to increased availability of both genetic and phenotypic patient. Particularly, in the area of cervical cancer intensive research investigates the role of specific genetic and environmental factors in determining the persistence of the HPV virus – which is the primary causal factor of cervical cancer – and the subsequent progression of the disease. To this direction, genetic association studies constitute a widely used scientific approach for medical research. However, despite the increased data availability worldwide, individual studies are often inconclusive due to the physical and conceptual isolation of the medical centers that limit the pool of data actually available to each researcher. ASSIST, an EU-funded research project, aims at facilitating medical research on cervical cancer by tackling these data isolation issues. To accomplish that, it virtually unifies multiple patient record repositories, physically located at different sites and subsequently employs inferencing techniques on the unified medical knowledge to enable the execution of cervical cancer related association studies that comprise both genotypic and phenotypic study factors, allowing medical researchers to perform more complex and reliable association studies on larger, high-quality datasets. @inproceedings{Mitkas2008ASSIST, |
| (C) | Pericles A. Mitkas, Vassilis Koutkias, Andreas L. Symeonidis, Manolis Falelakis, Christos Diou, Irini Lekka, Anastasios Delopoulos, Theodoros Agorastos and Nicos Maglaveras
"Association Studies on Cervical Cancer Facilitated by Inference and Semantic Technologes: The ASSIST Approach"
Proceedings of the International Congress of the European Federation for Medical Informatics (MIE08), Goteborg, Sweden, 2008 May
[Abstract][BibTex] Cervical cancer (CxCa) is currently the second leading cause of cancer-related deaths, for women between 20 and 39 years old. As infection by the human papillomavirus (HPV) is considered as the central risk factor for CxCa, current research focuses on the role of specific genetic and environmental factors in determining HPV persistence and subsequent progression of the disease. ASSIST is an EU-funded research project that aims to facilitate the design and execution of genetic association studies on CxCa in a systematic way by adopting inference and semantic technologies. Toward this goal, ASSIST provides the means for seamless integration and virtual unification of distributed and heterogeneous CxCa data repositories, and the underlying mechanisms to undertake the entire process of expressing and statistically evaluating medical hypotheses based on the collected data in order to generate medically important associations. The ultimate goal for ASSIST is to foster the biomedical research community by providing an open, integrated and collaborative framework to facilitate genetic association studies. @inproceedings{Mitkas2008Association, |
| (C) | Angelos Tsolakis, Manolis Falelakis and Anastasios Delopoulos
"A framework for efficient correspondence using feature interrelations"
19th International Conference on Pattern Recognition, 2008. ICPR 2008, pp. 1-4, IEEE, Tampa, FL, 2008 Dec
[Abstract][BibTex][pdf] We propose a formulation for solving the point pattern correspondence problem, relying on transformation invariants. Our approach can accommodate any degree of descriptors thus modeling any kind of potential deformation according to the needs of each specific problem. Other potential descriptors such as color or local appearance can also be incorporated. A brief study on the complexity of the methodology is made which proves to be inherently polynomial while allowing for further adjustments via thresholding. Initial experiments on both synthetic and real data demonstrate its potentials in terms of accuracy and robustness to noise and outliers. @inproceedings{Tsolakis2008Framework, |
2008
| (I) | Christos Diou, Nikos Batalas and Anastasios Delopoulos
"Advances in Semantic Media Adaptation and Personalization"
Charpter:Indexing and Browsing of Color Images: Design Considerations, 29, pp. 329-346, Springer, Berlin, Heidelberg, 2008 Jan
[Abstract][BibTex][pdf] This chapter deals with the various problems and decisions associated with the design of a content based image retrieval system. Image descriptors and descriptor similarity measures, indexing data structures and navigation approaches are examined through the evaluation of a set representative methods. Insight is provided regarding their e?ciency and applicability. Furthermore the accuracy of using low dimensional FastMap point con?gurations for indexing is extensively evaluated through a set of experiments. While it is out of the scope of this chapter to o?er a review of state of the art techniques in the problems above, the results presented aim at assisting in the design and development of practical, usable and possibly large scale image databases. @inbook{Diou2008Indexing, |
2007
| (J) | Anatasios Delopoulos, Levon Sukissian and Stefanos Kollias
"An efficient multiresolution texture classification scheme using neural networks"
International Journal of Computer Mathematics, 67, (1-2), pp. 155-168, 2007 Mar
[Abstract][BibTex][pdf] An efficient multiresolution texture classification method is proposed in this paper, based on 2-D linear prediction, multiresolution decomposition and artificial neural networks. A multiresolution spectral analysis of textured images is first developed, which permits 2-D AR texture modelling to be performed in multiple resolutions. Recursive estimation algorithms combined witth the Itakura distance measure provide sets of AR model parameters representing different textures at various resolutions. Appropriate neural network banks are constructed and trained being then able to effectively perform classification of textures irrespective of their resolution level. Results are presented using real textured images which illustrate the good performance of the proposed approach. @article{Delopoulos2007Efficient, |
2006
| (J) | Manolis Falelakis, Christos Diou and Anastasios Delopoulos
"Semantic identification: balancing between complexity and validity"
EURASIP Journal on Applied Signal Processing, pp. 183-183, 2006 Jan
[Abstract][BibTex][pdf] An efficient scheme for identifying semantic entities within data sets such as multimedia documents, scenes, signals, and so forth, is proposed in this work. Expression of semantic entities in terms of syntactic properties is modelled with appropriately defined finite automata, which also model the identification procedure. Based on the structure and properties of these automata, formal definitions of attained validity and certainty and also required complexity are defined as metrics of identification efficiency. The main contribution of the paper relies on organizing the identification and search procedure in a way that maximizes its validity for bounded complexity budgets and reversely minimizes computational complexity for a given required validity threshold. The associated optimization problem is solved by using dynamic programming. Finally, a set of experiments provides insight to the introduced theoretical framework. @article{Falelakis2006Semantic, |
| (J) | M. Wallace, T. Athanasiadis, Y. Avrithis, A. N. Delopoulos and S. Kollias
"Integrating multimedia archives: the architecture and the content layer"
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 36, (1), pp. 34-52, 2006 Jan
[Abstract][BibTex][pdf] In the last few years, numerous multimedia archives have made extensive use of digitized storage and annotation technologies. Still, the development of single points of access, providing common and uniform access to their data, despite the efforts and accomplishments of standardization organizations, has remained an open issue as it involves the integration of various large-scale heterogeneous and heterolingual systems. This paper describes a mediator system that achieves architectural integration through an extended three-tier architecture and content integration through semantic modeling. The described system has successfully integrated five multimedia archives, quite different in nature and content from each other, while also providing easy and scalable inclusion of more archives in the future. @article{Wallace2006Integrating, |
2006
| (C) | Nikos Batalas, Christos Diou and Anastasios Delopoulos
"Efficient Indexing, Color Descriptors and Browsing in Image Databases"
1st International Workshop on Semantic Media Adaptation and Personalization (SMAP06), pp. 129-134, Athens, Greece, 2006 Jan
[Abstract][BibTex][pdf] This work provides an experimental evaluation of various existing approaches for some of the major problems content based image retrieval applications are faced with. More specifically, global color representation, indexing and navigation methods are analyzed and insight is provided regarding their efficiency and applicability. Furthermore this paper proposes and evaluates the combined use of FastMap and kd-trees to enable accurate and fast retrieval in image databases. @inproceedings{Batalas2006Efficient, |
| (C) | Christos Diou, Giorgos Katsikatsos and Anastasios Delopoulos
"Constructing Fuzzy Relations from WordNet for Word Sense Disambiguation"
Semantic Media Adaptation and Personalization, pp. 135-140, Athens, Greece, 2006 Dec
[Abstract][BibTex][pdf] In this work, the problem of word sense disambiguation is formulated as a problem of imprecise associations between words and word senses in a textual context. The approach has two main parts. Initially, we consider that for each sense, a fuzzy set is given that provides the degrees of association between a number of words and the sense. An algorithm is provided that ranks the senses of a word in a text based on this information, effectively leading to word sense disambiguation. In the second part, a method based on WordNet is developed that constructs the fuzzy sets for the senses (independent of any text). Algorithms are provided that can help in both understanding and implementation of the proposed approach. Experimental results are satisfactory and show that modeling word sense disambiguation as a problem of imprecise associations is promising @inproceedings{Diou2006Constructing, |
| (C) | Christos Diou, Anastasia Manta and Anastasios Delopoulos
"Space-time tubes and motion representation"
Proceedings of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI), Athens, Greece, 2006 Jan
[Abstract][BibTex][pdf] Space-time tubes, a feature that can be used for analysis of motion based on the observed moving points in a scene is introduced. Information provided by sensors is used to detect moving points and based on their connectivity, tubes enable a structured approach towards identifying moving objects and high level events. It is shown that using tubes in conjunction with domain knowledge can overcome errors caused by the inaccuracy or inadequacy of the original motion information. The detected high level events can then be mapped to small natural language descriptions of object motion in the scene. @inproceedings{Diou2006Space, |
| (C) | Pericles A. Mitkas, Anastasios N. Delopoulos, Andreas L. Symeonidis and Fotis E. Psomopoulos
"A Framework for Semantic Data Integration and Inferencing on Cervical Cancer"
Hellenic Bioinformatics and Medical Informatics Meeting, Biomedical Research Foundation, Academy of Athens, Greece, 2006 Oct
[Abstract][BibTex][pdf] Advances in the area of biomedicine and bioengineering have allowed for more accurate and detailed data acquisition in the area of health care. Examinations that once were time- and cost-forbidding, are now available to public, providing physicians and clinicians with more patient data for diagnosis and successful treatment. These data are also used by medical researchers in order to perform association studies among environmental agents, virus characteristics and genetic attributes, extracting new and interesting risk markers which can be used to enhance early diagnosis and prognosis. Nevertheless, scientific progress is hindered by the fact that each medical center operates in relative isolation, regarding datasets and medical effort, since there is no universally accepted archetype/ontology for medical data acquisition, data storage and labeling. This, exactly, is the major goal of ASSIST: to virtually unify multiple patient record repositories, physically located at different laboratories, clinics and/or hospitals. ASSIST focuses on cervical cancer and implements a semantically-aware integration layer that unifies data in a seamless manner. Data privacy and security are ensured by techniques for data anonymization, secure data access and storage. Both the clinician as well as the medical researcher will have access to a knowledge base on cervical cancer and will be able to perform more complex and elaborate association studies on larger groups. @inproceedings{Mitkas2006Framework, |
2005
| (C) | Niki Aifanti and Anastasios Delopoulos
"Fuzzy-logic Based Information Fusion for Image Segmentation"
IEEE International Conference on Image Processing 2005, 2005 Sep
[Abstract][BibTex][pdf] This work presents an information fusion mechanism for image segmentation using multiple cues. Initially, a fuzzy clustering of each cue space is performed and corresponding membership functions are produced on the image coordinates space. The latter include complementary as well as redundant information. A fuzzy inference mechanism is developed, which exploits these characteristics and fuses the membership functions. The produced aggregate membership functions represent objects, which bear combinations of the properties specified by the cues. The segmented image results after post-processing and defuzzification, which involves majority voting. A fuzzy rule based merging algorithm is finally proposed for reducing possible oversegmentation. Experimental results have been included to illustrate the steps and the efficiency of the algorithm. @inproceedings{Aifanti2005Fuzzy, |
| (C) | Christos Diou, Manolis Falelakis and Anastasios Delopoulos
"Knowledge Based Unification of Medical Archives"
International Networking Conference (INC2005), Samos, Greece, 2005 Jan
[Abstract][BibTex] @inproceedings{Diou2005Knowledge, |
| (C) | Manolis Falelakis, Christos Diou, Anastasios Valsamidis and Anastasios Delopoulos
"Complexity Control in Semantic Identification"
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE05), pp. 102-107, Reno, Nevada, USA, 2005 Jan
[Abstract][BibTex][pdf] This paper proposes a methodology for modeling the process of semantic identification and controlling its complexity and accuracy of the results. Each semantic entity is defined in terms of lower level semantic entities and low level features that can be automatically extracted, while different membership degrees are assigned to each one of the entities participating in a definition, depending on their importance for the identification. By selecting only a subset of the features that are used to define a semantic entity both complexity and accuracy of the results are reduced. It is possible, however, to design the identification using the metrics introduced, so that satisfactory results are obtained, while complexity remains below some required limit. @inproceedings{Falelakis2005Complexity, |
| (C) | Manolis Falelakis, Christos Diou, Anastasios Valsamidis and Anastasios Delopoulos
"Dynamic Semantic Identification with Complexity Constraints as a Knapsack Problem"
The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05., IEEE, 2005 May
[Abstract][BibTex][pdf] The process of automatic identification of high level semantic entities (e.g., objects, concepts or events) in multimedia documents requires processing by means of algorithms that are used for feature extraction, i.e. low level information needed for the analysis of these documents at a semantic level. This work copes with the high and often prohibitive computational complexity of this procedure. Emphasis is given to a dynamic scheme that allows for efficient distribution of the available computational resources in application. Scenarios that deal with the identification of multiple high level entities with strict simultaneous restrictions, such as real time applications. @inproceedings{Falelakis2005Dynamic, |
| (C) | Manolis Falelakis, Christos Diou, Manolis Wallace and Anastasios Delopoulos
"Minimizing Uncertainty In Semantic Identification When Computing Resources Are Limited"
International Conference on Artificial Neural Networks (ICANN05), pp. 817-822, Springer Berlin Heidelberg, Warsaw, Poland, 2005 Jan
[Abstract][BibTex][pdf] In this paper we examine the problem of automatic semantic identi?cation of entities in multimedia documents from a computing point of view. Speci?cally, we identify as main points to consider the storage of the required knowledge and the computational complexity of the handling of the knowledge as well as of the actual identi?cation process. In order to tackle the above we utilize (i) a sparse representation model for storage, (ii) a novel transitive closure algorithm for handling and (iii) a novel approach to identi?cation that allows for the speci?cation of computational boundaries. @inproceedings{Falelakis2005Minimizing, |
2005
| (I) | Anastasios Delopoulos
"Multimedia Coding Techniques for Wireless Networks"
Charpter:2, 29, pp. 15-47, Wiley, 2005 Jan
[Abstract][BibTex][pdf] This chapter deals with the various problems and decisions associated with the design of a content based image retrieval system. Image descriptors and descriptor similarity measures, indexing data structures and navigation approaches are examined through the evaluation of a set representative methods. Insight is provided regarding their e?ciency and applicability. Furthermore the accuracy of using low dimensional FastMap point con?gurations for indexing is extensively evaluated through a set of experiments. While it is out of the scope of this chapter to o?er a review of state of the art techniques in the problems above, the results presented aim at assisting in the design and development of practical, usable and possibly large scale image databases. @inbook{Delopoulos2005Multimedia, |
2004
| (C) | Manolis Falelakis, Christos Diou and Anastasios Delopoulos
"Identification of Semantics: Balancing between Complexity and Validity"
2004 IEEE 6th Workshop on Multimedia Signal Processing, pp. 434-437, IEEE, Siena, Italy, 2004 Sep
[Abstract][BibTex][pdf] This paper addresses the problem of identifying semantic entities (e.g., events, objects, concepts etc.) in a particular environment (e.g., a multimedia document, a scene, a signal etc.) by means of an appropriately modelled semantic encyclopedia. Each semantic entity in the encyclopedia is defined in terms of other semantic entities as well as low level features, which we call syntactic entities, in a hierarchical scheme. Furthermore, a methodology is introduced, which can be used to evaluate the direct contribution of every syntactic feature of the document to the identification of semantic entities. This information allows us to estimate the quality of the result as well as the required computational cost of the search procedure and to balance between them. Our approach could be particularly important in real time and/or bulky search/indexing applications. @inproceedings{Falelakis2004Identification, |
| (C) | Panagiotis Panagiotopoulos, Manolis Falelakis and Anastasios Delopoulos
"Efficient Semantic Search Using Finite Automata"
6th COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, 2004 Jan
[Abstract][BibTex][pdf] An efficient scheme for identifying Semantic Entities within data sets such as multimedia documents, scenes, signals etc. is proposed in this work. Expression of Semantic Entities in terms of Syntactic Properties is proved to be isomorphic to appropriately defined finite automata, which also model the identification procedure. Based on the structure and properties of these automata, formal definitions of attained Validity and Certainty and also required Complexity are defined as metrics of identification efficiency. The main contribution of the paper relies on organizing the identification and search procedure in a way that maximizes its validity for bounded Complexity budgets and reversely minimizes computational Complexity for a given required Validity threshold. @inproceedings{Panagiotopoulos2004Efficient, |
2003
| (J) | C. Diou, Karwatka and Jacek
"Some methods of identification high clutter regions in radar tracking system"
Postepy Radiotechniki, 48, (147), pp. 3-15, 2003 Jan
[Abstract][BibTex] @article{Diou2003Some, |
2002
| (C) | Yannis Avrithis, Giorgos Stamou, Anastasios Delopoulos and Stefanos Kollias
"Intelligent Semantic Access to Audiovisual Content"
2nd Hellenic Conference on Artificial Intelligence (SETN-02), pp. 11-12, 2002 Jan
[Abstract][BibTex][pdf] In this paper, an integrated information system is presented that offers enhanced search and retrieval capabilities to users of heterogeneous digital audiovisual (a/v) archives. This novel system exploits the advances in handling a/v content and related metadata, as introduced by MPEG-4 and worked out by MPEG-7, to offer advanced access services characterized by the tri-fold “semantic phrasing of the request (query)”, “unified handling ” and “personalized response”. The proposed system is targeting the intelligent extraction of semantic information from a/v and text related data taking into account the nature of useful queries that users may issue, and the context determined by user profiles. From a technical point of view, it will play the role of an intermediate access server residing between the end users and multiple heterogeneous audiovisual archives organized according to new MPEG standards. @inproceedings{Avrithis2002Intelligent, |
2001
| (J) | Anastasios Delopoulos, Stephanos Kollias, Yiannis Avrithis, W. Haas and K. Majcen
"Unified Intelligent Access to Heterogeneous Audiovisual Content"
Content-Based Multimedia Indexing, 2001 Sep
[Abstract][BibTex][pdf] Content-based audiovisual data retrieval utilizing new emerging related standards such as MPEG-7 will yield ineffective results, unless major focus is given to the semantic information level. Mapping of low level, sub-symbolic descriptors of a/v archives to high level symbolic ones is in general difficult, even impossible with the current state of technology. It can, however, be tackled when dealing with specific application domains. It seems that the extraction of semantic information from a/v and text related data is tractable taking into account the nature of useful queries that users may issue. And the context determined by user profile. The European IST project FAETHON is developing a novel platform, that intends to exploit the aforementioned ideas in order to offer user friendly, highly informative access to distributed audiovisual archives. @article{Delopoulos2001Unified, |
| (J) | Christos Papachristou and Fotini-Niovi Pavlidou
"Collision-Free Operation in Ad Hoc Carrier Sense Multiple Access Wireless Networks"
IEEE Communications Letters, 6, (8), pp. 352-354, 2001 Aug
[Abstract][BibTex][pdf] IEEE standards;carrier sense multiple access;packet radio networks;telecommunication standards;CSMA/CA algorithm;IEEE 802.11 standard packets;RTS/CTS packets;ad hoc carrier sense multiple access wireless networks;busy energy bursts;collision-free operation;energy bursts packet delays;system loads;system performance;Ad hoc networks;Delay;Intelligent networks;Multiaccess communication;Road accidents;Sections;System performance;Telecommunication traffic;Wireless LAN;Wireless networks. @article{Papachristou, |
| (J) | Yiannis S. Xirouhakis, Athanasios I. Drosopoulos and Anastasios N. Delopoulos
"Efficient optical camera tracking in virtual sets"
IEEE Transactions on Image Processing, 10, (4), pp. 609-622, 2001 Apr
[Abstract][BibTex][pdf] Optical tracking systems have become particularly popular in virtual studios applications tending to substitute electromechanical ones. However, optical systems are reported to be inferior in terms of accuracy in camera motion estimation. Moreover, marker-based approaches often cause problems in image/video compositing and impose undesirable constraints on camera movement, present work introduces a novel methodology for the construction of a two-tone blue screen, which allows the localization of camera in three-dimensional (3-D) space on the basis of the captured sequence. At the same time, a novel algorithm is presented for the extraction of camera\\\'s 3-D motion parameters based on 3-D-to-two-dimensional (2-D) line correspondences. Simulated experiments have been included to illustrate the performance of the proposed system. @article{Xirouhakis2001Efficient, |
2001
| (C) | Giorgos Akrivas, Spiros Ioannou, Elias Karakoulakis, Kostas Karpouzis, Yannis Avrithis, Anastasios Delopoulos, Stefanos Kollias, Iraklis Varlamis and Michalis Vaziriannis
"An Intelligent System for Retrieval and Mining of Audiovisual Material Based on the MPEG-7 Description Schemes"
Proceedings of European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems, Tenerife, Spain, 2001 Dec
[Abstract][BibTex][pdf] audiovisual archives, multimedia databases, multimedia description schemes, retrieval and mining of audiovisual data @inproceedings{Akrivas2001Intelligent, |
| (C) | Fotini-Niovi Pavlidou and Christos Papachristou
"Collision-Free Operation in Ad Hoc Carrier Sense Multiple Access Wireless Networks"
International Symposium on 3rd Generation Infrastructures and Services (3GIS), Athens, Greece, 2001 Jan
[Abstract][BibTex] @inproceedings{Pavlidou2001Collision, |
| (C) | Gabriel Tsechpenakis, Yiannis Xirouhakis and Anastasios Delopoulos
"A Multiresolution Approach for Main Mobile Object Localization in Video Sequences"
International Workshop on Very Low Bitrate Video Coding, 2001 Nov
[Abstract][BibTex][pdf] Main mobile object localization is a task that emerges in research fields such us video understanding, object-based coding and various related applications, such as content- based retrieval, remote surveillance and object recognition. The present work revisits mobile object localization in the context of content-based retrieval schemes and the related MPEG-7 framework, for natural and synthetic, indoor and outdoor sequences, when either a static or a mobile camera is utilized. The proposed multiresolution approach greatly improves the trade-off between accuracy and time- performance leading to satisfactory results with a considerably low amount of computations. Moreover, based on the point gatherings extracted in (14), the bounding polygon and the direction of movement are estimated for each mobile object; thus yielding an adequate representation in the MPEG-7 sense. Finally, the resulting polygons can be used as appropriate initial estimates for methods that extract object contours, e.g. curve propagation approaches, such as (8,10) which utilize the level-set method . Experimental results over a number of distinct natural sequences have been included to illustrate the performance of the proposed approach. @inproceedings{Tsechpenakis2001Multiresolution, |
2000
| (J) | Yiannis Xirouhakis and Anastasios Delopoulos
"A Comparative Study on 3D Motion Estimation under Orthography"
Nordic signal processing symposium, 2000 Jun
[Abstract][BibTex][pdf] In the present work, the algorithm proposed in [8,10] is tested against existing approaches on 3D motion and structure estimation of rigid objects under orthography. The theoretical relation between the proposed approach and the well-known factorization and epipolar methods is discussed. At the same time, comparative simulated experiments are given, illustrating the performance of the three algorithms (the factorization, the epipolar and the proposed one). The proposed algorithm seems to be more genericthan the existing approaches, and provides superior estimates of 3D motion in most cases. @article{Xirouhakis2000Comparative, |
| (J) | Yiannis Xirouhakis and Anastasios Delopoulos
"Least Squares Estimation of 3D Shape and Motion of Rigid Objects from their Orthographic Projections"
IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, (4), pp. 393-399, 2000 Apr
[Abstract][BibTex][pdf] The extraction of motion and shape information of three-dimensional objects from their two-dimensional projections is a task that emerges in various applications such as computer vision, biomedical engineering, and video coding and mining especially after the recent guidelines of the Motion Pictures Expert Group regarding MPEG-4 and MPEG-7 standards. Present work establishes a novel approach for extracting the motion and shape parameters of a rigid three-dimensional object on the basis of its orthographic projections and the associated motion field. Experimental results have been included to verify the theoretical analysis. @article{Xirouhakis2000Least, |
2000
| (C) | Athanasios Drosopoulos, Yiannis Xirouhakis and Anastasios Delopoulos
"Optical Camera Tracking in Virtual Studios: Degenerate Cases"
Pattern Recognition, pp. 1114-1117, IEEE, Barcelona, 2000 Sep
[Abstract][BibTex][pdf] Over the past few years, virtual studios applications have significantly attracted the attention of the entertainment industry. Optical tracking systems for virtual sets production have become particularly popular tending to substitute electro-mechanical ones. In this work, an existing optical tracking system is revisited, in order to tackle with inherent degenerate cases; namely, reduction of the perspective projection model to the orthographic one and blurring of the blue screen. In this context, we propose a simple algorithm for 3D motion estimation under orthography using 3D-to-2D line correspondences. In addition, the watershed algorithm is employed for successful feature extraction in the presence of defocus or motion blur. @inproceedings{Drosopoulos2000Optical, |
| (C) | Gabriel Tsechpenakis, Yiannis Xirouhakis and Anastasios Delopoulos
"Main Mobile Object Detection and Localization in Video Sequences"
Advances in Visual Information Systems, pp. 84-95, Lyon, France, 2000 Nov
[Abstract][BibTex][pdf] Main mobile object detection and localization is a task of major importance in the fields of video understanding, object-based coding and numerous related applications, such as content-based retrieval, remote surveillance and object recognition. The present work revisits the algorithm proposed in [13] for mobile object localization in both indoor and outdoor sequences when either a static or a mobile camera is utilized. The proposed approach greatly improves the trade-off between accuracy and time-performance leading to satisfactory results with a considerably low amount of computations. Moreover, based on the point gatherings extracted in [13], the bounding polygon and the direction of movement are estimated for each mobile object; thus yielding an adequate representation in the MPEG-7 sense. Experimental results over a number of distinct natural sequences have been included to illustrate the performance of the proposed approach. @inproceedings{Tsechpenakis2000Main, |
1999
| (J) | Sotirios Pavlopoulos and Anastasios Delopoulos
"Designing and implementing the transition to a fully digital hospital"
IEEE Transactions on Information Technology in Biomedicine, 3, (1), pp. 6-19, 1999 Mar
[Abstract][BibTex][pdf] The increase in the number of examinations performed in modern healthcare institutions in conjunction with the range of imaging modalities available today have resulted in a tremendous increase in the number of medical images generated and has made the need for a dedicated system able to acquire, distribute, and store medical image data very attractive. Within the framework of the Hellenic R&D program, we have designed and implemented a picture archiving and communication system for a high-tech cardiosurgery hospital in Greece. The system is able to handle in a digital form images produced from ultrasound, X-ray angiography, ?-camera, chest X-rays, as well as electrocardiogram signals. Based on the adoption of an open architecture highly relying on the DICOM standard, the system enables the smooth transition from the existing procedures to a fully digital operation mode and the integration of all existing medical equipment to the new central archiving system. @article{Pavlopoulos1999Designing, |
1999
| (C) | Yiannis Xirouhakis, George Votsis and Anastasios Delopoulos
"Estimation of 3D Motion and Structure of Human Faces"
Advances in Intelligent Systems: Concepts, Tools and Applications, pp. 333-344, Springer Netherlands, 1999 Jan
[Abstract][BibTex][pdf] The extraction of motion and shape information of three dimensional objects from video sequences emerges in various applications especially within the framework of the MPEG-4 and MPEG-7 standards. Particular attention has been given to this problem within the scope of model-based coding and knowledge-based ZD modeling. In this chapter, a novel algorithm is proposed for the 3D reconstruction of a human face from 2D projections. The obtained results can contribute to several fields with an emphasis on 3D modeling and characterization of human faces. @inproceedings{Xirouhakis1999Estimation, |
| (C) | Yiannis Xirouhakis, Gabriel Tsechpenakis and Anastasios Delopoulos
"User Choices for Efficient 3D Motion and Shape Extraction from Orthographic Projections"
The 6th IEEE International Conference on Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99, pp. 1261- 264, Pafos, 1999 Sep
[Abstract][BibTex][pdf] The extraction of structure-from-motion emerges in several research fields such as computer vision, video coding, biomedical engineering and human-computer interaction. The present work focuses on the algorithmic approach of structure-from-motion extraction under orthography providing, at the same time, guidelines in matters of implementation. Relative principles, constraints and stability are discussed. The improvement of the algorithm\'s performance w.r.t. the proposed user-choices is illustrated by means of experimental results. @inproceedings{Xirouhakis1999User, |
1998
| (J) | Stefanos Kollias and Anastasios Delopoulos
"Multiresolution Techniques and their Applications to Image Recognition"
Expert Systems Techniques and Applications, 1998 Jan
[Abstract][BibTex] @article{Kollias1998Multiresolution, |
1998
| (C) | Yannis S. Avrithis, Anastasios N. Delopoulos and Vassilios N. Alexopoulos
"Ultrasonic Array Imaging Using CDMA Techniques"
9th European Signal Processing Conference (EUSIPCO 1998), Rhodes, 1998 Sep
[Abstract][BibTex][pdf] A new method for designing ultrasonic imaging systems is presented in this paper. The method is based on the use of transducer arrays whose elements transmit wideband signals generated by pseudo-random codes, similarly to code division multiple access (CDMA) systems in communications. The use of code sequences instead of pulses, which are typically used in conventional phased arrays, combined with transmit and receive beamforming for steering different codes at each direction, permits parallel acquisition of a large number of measurements corresponding to different directions. Significantly higher image acquisition rate as well as lateral and contrast resolution are thus obtained, while axial resolution remains close to that of phased arrays operating in pulse-echo mode. Time and frequency division techniques are also studied and a unified theoretical model is derived, which is validated by experimental results. @inproceedings{Avrithis1998Ultrasonic, |
| (C) | Anastasios Delopoulos, Yiannis XirouhakisRobust Estimation of Motion and Shape based on Orthographic Projections of Rigid Objects
"Robust Estimation of Motion and Shape based on Orthographic Projections of Rigid Objects"
IEEE Tenth Image and Multidimensional Signal Processing Workshop - IMDSP'98, IEEE, 1998 Jan
[Abstract][BibTex] @inproceedings{Delopoulos1998Robust, |
| (C) | Anastasios Doulamis, Nikolaos Doulamis and Anastasios Delopoulos
"Optimal Subband Analysis Filters Compensating for Quantization and Additive Noise"
9th European Signal Processing Conference (EUSIPCO 1998, IEEE, Rhodes, 1998 Sep
[Abstract][BibTex][pdf] In this paper, we present an analysis filter design technique which optimally defines the proper decimator so that the quantization noise is compensated. The analysis is based on a distortion criterion minimization using the Lagrange multipliers. The optimal decimation filters are derived through a Ricatti solution which involves both the quantization and the interpolation filters. Experimental results are presented indicating the good performance of the proposed technique versus conventional subband filter banks in the presence of quantization noise. @inproceedings{Doulamis1998Optimal, |
1997
| (C) | Anastasios Delopoulos and Maria Rangoussi
"Cumulants of a Multidimensional Process Observed at Rationally Related Resolutions"
International Workshop on Sampling Theory and Applications, Aveiro, Protugal, 1997 Jan
[Abstract][BibTex] @inproceedings{Delopoulos1997Cumulants, |
| (C) | Anastasios Delopoulos and Maria Rangoussi
"The Fractal Behaviour of Unvoiced Plosives: A Means for Classification"
5th European Conference on Speech Communication and Technology (EUROSPEECH'97), Rhodes, Greece, 1997 Jan
[Abstract][BibTex] @inproceedings{Delopoulos1997Fractal, |
| (C) | Anastasios Delopoulos, Maria Rangoussi and Demetrios Kalogeras
"Fractional Sampling Rate Conversion in the 3rd Order Cumulant domain and Applications"
1997 13th International Conference on Digital Signal Processing Proceedings, 1997. DSP 97, pp. 157-160, IEEE, Santorini, Greece, 1997 Jul
[Abstract][BibTex][pdf] In a variety of problems a random process is observed at different resolutions while knowledge of the corresponding scale conversion ratio usually contains useful information related to problem-specific quantities. A method is proposed which exploits cumulant domain relations of such signals in order to yield fractional estimates of the unknown conversion ratio. The noise insensitivity and shift invariance property of the cumulants offers advantages to the proposed method over signal domain alternatives. These advantages are discussed in two classes of practical problems involving 1-D and 2-D scale converted signals. @inproceedings{Delopoulos1997Fractional, |
1996
| (J) | Anastasios Delopoulos and Georgios B. Giannakis
"Cumulant Based Identification of Noisy Closed-Loop Systems"
International journal of adaptive control and signal processing, 10, (2-3), pp. 303-317, 1996 Mar
[Abstract][BibTex][pdf] Conventional parameter estimation approaches fail to identify linear systems operating in closed loop when both input and output measurements are contaminated by additive noise of unknown (cross-)spectral characteristics. However, even in the absence of measurement noise, parameter estimation is involved owing to the additive system noise entering the loop. The present work introduces a novel criterion which is theoretically insensitive to a class of disturbances and yields the same parameter estimates that one obtains using mean squared error (MSE) minimization in the absence of noise. A strongly convergent sample-based approximation of the proposed criterion is introduced for consistent parameter estimation in practice. It is also shown that in the common case of ARMA modelling the resulting parameter estimates coincide with those obtained from a set of linear equations which can be solved using a time-recursive algorithm. Simulation results are presented to verify the performance of the proposed schemes in low-signal-to-noise-ratio environments. @article{Delopoulos1996Cumulant, |
| (J) | Anastasios Delopoulos and Stefanos Kollias
"Optimal filter banks for signal reconstruction from noisy subband components"
IEEE Transactions on Signal Processing, 44, (2), pp. 212-224, 1996 Feb
[Abstract][BibTex][pdf] Conventional design techniques for analysis and synthesis filters in subband processing applications guarantee perfect reconstruction of the original signal from its subband components. The resulting filters, however, lose their optimality when additive noise due, for example, to signal quantization, disturbs the subband sequences. We propose filter design techniques that minimize the reconstruction mean squared error (MSE) taking into account the second order statistics of signals and noise in the case of either stochastic or deterministic signals. A novel recursive, pseudo-adaptive algorithm is proposed for efficient design of these filters. Analysis and derivations are extended to 2-D signals and filters using powerful Kronecker product notation. A prototype application of the proposed ideas in subband coding is presented. Simulations illustrate the superior performance of the proposed filter banks versus conventional perfect reconstruction filters in the presence of additive subband noise. @article{Delopoulos1996Optimal, |
| (J) | Nikos G. Panagiotidis, Anastasios N. Delopoulos and Stefanos D. Kollias
"Application-driven computation of optimum quantization tables for DCT-based block coders"
Digital Compression Technologies and Systems for Video Communications, 617, pp. 617-622, 1996 Sep
[Abstract][BibTex][pdf] In this paper we propose a method for computing optimal quantization tables for specific images. The main criterion for this processing is the allocation of bandwidth in frequency subspaces in the DCT-domain according to power metrics obtained from the transform coefficients. Choice of the weights determines the subjective importance of each frequency coefficient as well as its contribution the finally perceived image. The simultaneous requirement that the quantization tables yield data compression comparable to the one achieved by the baseline JPEG scheme at various quality factors (QF) imposes an additional constraint to the proposed model. @article{Panagiotidis, |
1996
| (C) | Vassilios Alexopoulos, Anastasios Delopoulos and Stefanos Kollias
"Towards a Standardization for Medical Video Encoding and Archiving"
14th International EuroPacs Meeting, Heraklion, Crete, Greece, 1996 Jan
[Abstract][BibTex] @inproceedings{Alexopoulos1996Towards, |
| (C) | Anastasios Delopoulos, Dimitrios Kalogeras, Vassilios Alexopoulos and Stefanos Kollias
"Real Time MPEG-1 Video Transmission over Local Area Networks"
Multimedia Communications and Video Coding, pp. 47-55, Berlin, Germany, 1996 Oct
[Abstract][BibTex][pdf] In this work is presented the architecture of an MPEG-1 stream transmission system appropriate for point-to-point transfer of live video and audio over TCP/IP local area networks. The hardware and software modules of the system are presented as well. Experimental results on the statistical behavior of the generated and transmitted MPEG-1 stream are quoted. @inproceedings{Delopoulos1996Real, |
| (C) | Anastasios Delopoulos, Maria Rangoussi and Janne Andersen
"Recognition of voiced speech from the bispectrum"
8th European Signal Processing Conference, 1996. EUSIPCO 1996., IEEE, Trieste, Italy, 1996 Jan
[Abstract][BibTex][pdf] Recognition of voiced speech phonemes is addressed in this paper using features extracted from the bispectrum of the speech signal. Voiced speech is modeled as a superposition of coupled harmonics, located at frequencies that are multiples of the pitch and modulated by the vocal tract. For this type of signal, nonzero bispectral values are shown to be guaranteed by the estimation procedure employed. The vocal tract frequency response is reconstructed from the bispectrum on a set of frequency points that are multiples of the pitch. An AR model is next fitted on this transfer function. The AR coefficients are used as the feature vector for the subsequent classification step. Any finite dimension vector classifier can be employed at this point. Experiments using the LVQ neural classifier give satisfactory classification scores on real speech data, extracted from the DARPA/TIMIT speech corpus. @inproceedings{Delopoulos1996Recognition, |
1995
| (J) | Georgios B. Giannakis and Anastasios Delopoulos
"Cumulant based autocorrelation estimates of non-Gaussian linear processes"
Signal Processing, 47, (1), pp. 1-17, 1995 Nov
[Abstract][BibTex][pdf] Autocorrelation of linear random processes can be expressed in terms of their cumulants. Theoretical insensitivity of the latter to additive Gaussian noise of unknown covariance, is exploited in this paper to develop (within a scale) autocorrelation estimators of linear non-Gaussian time series using cumulants of order higher than two. Windowed projections of third-order cumulants are shown to yield strongly consistent estimators of the autocorrelation sequence. Both batch and recursive algorithms are derived. Asymptotic variance expressions of the proposed estimators are also presented. Simulations are provided to illustrate the performance of the proposed algorithms and compare them with conventional approaches. @article{Giannakis2000Cumulant, |
| (J) | Andreas Tirakis, Anastasios Delopoulos and Stefanos Kollias
"2-D Filter Bank Design for Optimal Reconstruction using Limited Subband Information"
IEEE Transanctions on Image Processing, 4, (8), pp. 1160-1165, 1995 Aug
[Abstract][BibTex][pdf] In this correspondence, we propose design techniques for analysis and synthesis filters of 2-D perfect reconstruction filter banks (PRFB\'s) that perform optimal reconstruction when a reduced number of subband signals is used. Based on the minimization of the squared error between the original signal and some low-resolution representation of it, the 2-D filters are optimally adjusted to the statistics of the input images so that most of the signal\'s energy is concentrated in the first few subband components. This property makes the optimal PRFB\'s efficient for image compression and pattern representations at lower resolutions for classification purposes. By extending recently introduced ideas from frequency domain principal component analysis to two dimensions, we present results for general 2-D discrete nonstationary and stationary second-order processes, showing that the optimal filters are nonseparable. Particular attention is paid to separable random fields, proving that only the first and last filters of the optimal PRFB are separable in this case. Simulation results that illustrate the theoretical achievements are presented. @article{Tirakis1995Filter, |
1995
| (C) | Maria Rangoussi and Anastasios Delopoulos
"Classification of Consonants using Wigner Distribution Features"
12th International Conference on DSP, Limassol, Cyprus, 1995 Jan
[Abstract][BibTex] @inproceedings{Rangoussi1995Classification, |
| (C) | Maria Rangoussi and Anastasios Delopoulos
"Recognition of Unvoiced Stops from their Time-Frequency Representation"
1995 International Conference on Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., pp. 792-795, IEEE, Detroit, MI, 1995 May
[Abstract][BibTex][pdf] The recognition of the unvoiced stop sounds /k/, /p/ and /t/ in a speech signal is an interesting problem, due to the irregular, aperiodic, nonstationary nature of the corresponding signals. Their spotting is much easier, however, thanks to the characteristic silence interval they include. Classification of these three phonemes is proposed, based on the patterns extracted from their time-frequency representation. This is possible because the different articulation points of /k/, /p/ and /t/ are reflected into distinct patterns of evolution of their spectral contents with time. These patterns can be obtained by suitable time-frequency analysis, and then used for classification. The Wigner distribution of the unvoiced stop signals, appropriately smoothed and subsampled, is proposed as the basic classification pattern. Finally, for the classification step, the learning vector quantization (LVQ) classifier of Kohonen (1988) is employed on a set of unvoiced stop signals extracted from the TIMIT speech database, with encouraging results under context- and speaker-independent testing conditions. @inproceedings{Rangoussi1995Recognition, |
1994
| (J) | Anastasios Delopoulos and Georgios B. Giannakis
"Consistent identification of stochastic linear systems with noisy input-output data"
Automatica, 30, (8), pp. 1271-1294, 1994 Aug
[Abstract][BibTex][pdf] A novel criterion is introduced for parametric errors-in-variables identification of stochastic linear systems excited by non-Gaussian inputs. The new criterion is (at least theoretically) insensitive to a class of input-output disturbances because it implicitly involves higher- than second-order cumulant statistics. In addition, it is shown to be equivalent to the conventional Mean-Squared Error (MSE) as if the latter was computed in the ideal case of noise-free input-output data. The sampled version of the criterion converges to the novel MSE and guarantees strongly consistent parameter estimators. The asymptotic behavior of the resulting parameter estimators is analyzed and guidelines for minimum variance experiments are discussed briefly. Informative enough input signals and persistent of excitation conditions are specified. Computatonally attractive Recursive-Least-Squares variants are also developed for on-line implementation of ARMA modeling, and their potential is illustrated by applying them to time-delay estimation in low SNR environment. The performance of the proposed algorithms and comparisons with conventional methods are corroborated using simulated data. @article{Delopoulos1994Consistent, |
| (J) | Anastasios Delopoulos, A. Tirakis and Stephanos Kollias
"Invariant image classification using triple-correlation-based neural networks"
IEEE Transactions on Neural Networks, 5, (3), pp. 392-408, 1994 May
[Abstract][BibTex][pdf] Triple-correlation-based neural networks are introduced and used in this paper for invariant classification of 2D gray scale images. Third-order correlations of an image are appropriately clustered, in spatial or spectral domain, to generate an equivalent image representation that is invariant with respect to translation, rotation, and dilation. An efficient implementation scheme is also proposed, which is robust to distortions, insensitive to additive noise, and classifies the original image using adequate neural network architectures applied directly to 2D image representations. Third-order neural networks are shown to be a specific category of triple-correlation-based networks, applied either to binary or gray-scale images. A simulation study is given, which illustrates the theoretical developments, using synthetic and real image data. @article{Delopoulos1994Invariant, |
1994
| (C) | Anastasios Delopoulos and Stefanos Kollias
"Optimal filterbanks for signal reconstruction from noisy subband components"
28th ASILOMAR Conference on Signals, Systems and Computers, Asilomar CA, USA, 1994 Jan
[Abstract][BibTex] @inproceedings{Delopoulos1994Optimal, |
| (C) | Nikolaos G. Panagiotidis, Anastasios Delopoulos and Stefanos D. Kollias
"Neural-Network Based Classification of Laser-Doppler Flowmetry Signals"
1994 IEEE Workshop on Neural Networks for Signal Processing, pp. 709-718, Ermioni, 1994 Sep
[Abstract][BibTex][pdf] Laser Doppler flowmetry is the most advantageous technique for non-invasive patient monitoring. Based on the Doppler principle, signals corresponding to blood flow are generated, and metrics corresponding to healthy vs. patient samples are extracted. A neural-network based classifier for these metrics is proposed. The signals are initially filtered and transformed into the frequency domain through third-order correlation and bispectrum estimation. The pictorial representation of the correlations is subsequently routed into a neural network based multilayer perceptron classifier, which is described in detail. Finally, experimental results demonstrating the efficiency of the proposed scheme are presented. @inproceedings{Panagiotidis1994Neural, |
| (C) | Andreas Tirakis, Anastasios Delopoulos and Stefanos Kollias
"Invariant Image Recognition Using Triple Correlations and Neural Networks"
IEEE International Conference on Neural Networks, pp. 4055-4060, Orlando FL, USA, 1994 Jun
[Abstract][BibTex] Triple-correlation-based image representations were previously (Delopoulos, Tirakis, and Kollias, 1994) combined with neural network architectures for deriving an invariant, with respect to translation, rotation and dilation, robust classification scheme. Efficient implementations are described in this paper, which reduce the computational complexity of the method. Hierarchical, multiresolution neural networks are proposed as an effective architecture for achieving this purpose. @inproceedings{Tirakis1994Invariant, |
1993
| (C) | Yannis Avrithis, Anastasios Delopoulos and Stefanos Kollias
"An Efficient Scheme for Invariant Optical Character Recognition Using Triple Correlations"
Proc. of twp-login.phphe Intl. Conf. on DSP and II Intl. Conf. on Comp. Applications to Engineering Systems, Nicosia, Cyprus, 1993 Jan
[Abstract][BibTex][pdf] The implementation of an efficient scheme for translation, rotation and scale invariant optical character recognition is presented in this paper. An image representation is used, which is based on appropriate clustering and transformation of the image triple-correlation domain. This representation is one-to-one related to the class of all shifted-rotated-scaled versions of the original image, as well as robust to a wide variety of additive noises. Special attention is given to binary images, which are used for Optical Character Recognition, and simulation results illustrate the performance of the proposed implementation. @inproceedings{Avrithis1993Efficient, |
| (C) | Maria Rangoussi, Anastasios Delopoulos and Michail K. Tsatsanis
"On the use of higher-order-statistics for robust endpoint detection of speech"
Proc. of 3rd Intl. Workshop on Higher Order Statistics, Lake Tahoe California, USA, 1993 Jan
[Abstract][BibTex] Third order statistics of speech signals are not identically zero, as it would be expected based on the linear model for voice. This is due to quadratic harmonic coupling produced in the vocal tract. Based on this observation, third order cumulants are employed to address the endpoint detection problem in low SNR level recordings due to their immunity to (colored) additive non-skewed noise. The proposed method uses the maximum singular value of an appropriately formed cumulant matrix to distinguish between voiced parts of the speech signal, and silence (noise). Adaptive implementations are also proposed, making this method computationally attractive. Results of batch and adaptive forms are presented for real and simulated data. @inproceedings{Rangoussi1993Higher, |
| (C) | Andreas Tirakis, Anastasios Delopoulos and Stefanos Kollias
"Neural-Network-Based image Classification Using Optimal Multiresolution Analysis"
Proc of Intl. Conference on NN and SP (NNASP'93), 1993 Jan
[Abstract][BibTex] @inproceedings{Tirakis1993Neural, |
1992
| (J) | Anastasios Delopoulos and Georgios B. Giannakis
"Strongly consistent identification algorithms and noise insensitive MSE criteria"
IEEE Transactions on Signal Processing, 40, (8), pp. 1955-1970, 1992 Aug
[Abstract][BibTex][pdf] Windowed cumulant projections of nonGaussian linear processes yield autocorrelation estimators which are immune to additive Gaussian noise of unknown covariance. By establishing strong consistency of these estimators, strongly consistent and noise insensitive recursive algorithms are developed for parameter estimation. These computationally attractive schemes are shown to be optimal with respect to a modified mean-square-error (MSE) criterion which implicitly exploits the high signal-to-noise ratio domain of cumulant statistics. The novel MSE objective function is expressed in terms of the noisy process, but it is shown to be a scalar multiple of the standard MSE criterion as if the latter was computed in the absence of noise. Simulations illustrate the performance of the proposed algorithms and compare them with the conventional algorithms. @article{Delopoulos1992Strongly, |
1992
| (C) | Anastasios Delopoulos, Andreas Tirakis and Stefanos Kollias
"Invariant image recognition using triple correlations"
Proc. of EUSIPCO-92, Brussels, Belgium, 1992 Jan
[Abstract][BibTex] @inproceedings{Delopoulos1992Invariant, |
| (C) | Andreas Tirakis, Anastasios Delopoulos and Stefanos Kollias
"Cumulant-based neural network classifiers"
Proc. of International Conference on Artificial Neural Networks ICANN92, Brighton, UK, 1992 Jan
[Abstract][BibTex] @inproceedings{Tirakis1992Cumulant, |
1991
| (C) | Anastasios Delopoulos and Georgios B. Giannakis
"Input Design for Consistent Identification in the Presence of Input/Output Noise"
Proc. of Intl. Signal Processing Workshop on Higher-Order Statistics, Chamrousse, France, 1991 Jan
[Abstract][BibTex] @inproceedings{Delopoulos1991Input, |
| (C) | Anastasios Delopoulos and Georgios B. Giannakis
"Strongly consistent output only and input-ouput identification in the presence of Gaussian noise"
Proc. of Intl. Conf. on ASSP,(ICASSP '91), Toronto, Canada, 1991 Jan
[Abstract][BibTex] @inproceedings{Delopoulos1991Strongly, |
1990
| (J) | Georgios Giannakis and Anastasios Delopoulos
"Nonparametric estimation of autocorrelation and spectra using cumulants and polyspectra"
Advanced Signal Processing Algorithms, Architectures, and Implementations, 503, 1990 Nov
[Abstract][BibTex][pdf] Autocorrelation and specira of linear random processes can be expressed in terms of cumulants and polyspectra respectively. The insensitivity of the latter to additive Gaussian noise of unknown covariance is exploited in this paper to develop spectral estimators of deterministic and linear non-Gaussian signals using polyspectra. In the time-domain windowed projections of third-order cumulants are shown to yield consistent estimators of the autocorrelation sequence. Both batch and recursive algorithms are derived. In the frequency-domain a Fourier-slice solution and a least-squares approach are described for performing spectral analysis through windowed bi-periodograms. Asymptotic variance expressions of the time- and frequencydomain estimators are also presented. Two-dimensional extensions are indicated and potential applications are discussed. Simulations are provided to illustrate the performance of the proposed algorithms and compare them with conventional approaches. @article{Giannakis1990Nonparametric, |
1990
| (C) | Anastasios Delopoulos and Georgios B. Giannakis
"Strongly consistent identification algorithms and noise insensitive MSE criteria"
Proc. of 4th Digital Signal Processing Workshop, New Paltz NY, USA, 1990 Jan
[Abstract][BibTex] @inproceedings{Delopoulos1990Strongly, |
| (C) | Georgios Giannakis and Anastasios Delopoulos
"Nonparametric estimation of autocorrelation and spectra using cumulants and polyspectra"
Proc. of Soc. of Photo-Opt. Instr. Eng., Advanced Signal Processing Alg., and Implem.(SPIE '90), 1990 Nov
[Abstract][BibTex][pdf] Autocorrelation and specira of linear random processes can be expressed in terms of cumulants and polyspectra respectively. The insensitivity of the latter to additive Gaussian noise of unknown covariance is exploited in this paper to develop spectral estimators of deterministic and linear non-Gaussian signals using polyspectra. In the time-domain windowed projections of third-order cumulants are shown to yield consistent estimators of the autocorrelation sequence. Both batch and recursive algorithms are derived. In the frequency-domain a Fourier-slice solution and a least-squares approach are described for performing spectral analysis through windowed bi-periodograms. Asymptotic variance expressions of the time- and frequencydomain estimators are also presented. Two-dimensional extensions are indicated and potential applications are discussed. Simulations are provided to illustrate the performance of the proposed algorithms and compare them with conventional approaches. @inproceedings{Giannakis1990, |