| Records |
| Author |
Jose Seabra; Francesco Ciompi; Oriol Pujol; J. Mauri; Petia Radeva; Joao Sanchez |
| Title |
Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound |
Type |
Journal Article |
| Year |
2011 |
Publication |
IEEE Transactions on Biomedical Engineering |
Abbreviated Journal  |
TBME |
| Volume |
58 |
Issue |
5 |
Pages |
1314-1324 |
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| Abstract |
Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology. |
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MILAB;HuPBA |
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no |
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Admin @ si @ SCP2011 |
Serial |
1712 |
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| Author |
Marina Alberti; Simone Balocco; Carlo Gatta; Francesco Ciompi; Oriol Pujol; Joana Silva; Xavier Carrillo; Petia Radeva |
| Title |
Automatic Bifurcation Detection in Coronary IVUS Sequences |
Type |
Journal Article |
| Year |
2012 |
Publication |
IEEE Transactions on Biomedical Engineering |
Abbreviated Journal |
TBME |
| Volume |
59 |
Issue |
4 |
Pages |
1022-2031 |
| Keywords |
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| Abstract |
In this paper, we present a fully automatic method which identifies every bifurcation in an intravascular ultrasound (IVUS) sequence, the corresponding frames, the angular orientation with respect to the IVUS acquisition, and the extension. This goal is reached using a two-level classification scheme: first, a classifier is applied to a set of textural features extracted from each image of a sequence. A comparison among three state-of-the-art discriminative classifiers (AdaBoost, random forest, and support vector machine) is performed to identify the most suitable method for the branching detection task. Second, the results are improved by exploiting contextual information using a multiscale stacked sequential learning scheme. The results are then successively refined using a-priori information about branching dimensions and geometry. The proposed approach provides a robust tool for the quick review of pullback sequences, facilitating the evaluation of the lesion at bifurcation sites. The proposed method reaches an F-Measure score of 86.35%, while the F-Measure scores for inter- and intraobserver variability are 71.63% and 76.18%, respectively. The obtained results are positive. Especially, considering the branching detection task is very challenging, due to high variability in bifurcation dimensions and appearance. |
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0018-9294 |
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MILAB;HuPBA |
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no |
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Admin @ si @ ABG2012 |
Serial |
1996 |
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Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas |
| Title |
Assessing the Influence of Mirroring on the Perception of Professional Competence using Wearable Technology |
Type |
Journal Article |
| Year |
2016 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
9 |
Issue |
2 |
Pages |
161-175 |
| Keywords |
Mirroring; Nodding; Competence; Perception; Wearable Technology |
| Abstract |
Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring
events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist’s nods is a better predictor than the number of customer’s nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras. |
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LAMP; 600.072; |
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no |
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Admin @ si @ MTR2016 |
Serial |
2826 |
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| Author |
Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
Audio-Visual Emotion Recognition in Video Clips |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
10 |
Issue |
1 |
Pages |
60-75 |
| Keywords |
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| Abstract |
This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. |
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1 Jan.-March 2019 |
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HUPBA; 602.143; 602.133 |
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no |
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Admin @ si @ NMN2017 |
Serial |
3011 |
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Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier |
| Title |
Multimodal First Impression Analysis with Deep Residual Networks |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
8 |
Issue |
3 |
Pages |
316-329 |
| Keywords |
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| Abstract |
People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations |
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HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ GGB2018 |
Serial |
3210 |
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| Author |
Ricardo Dario Perez Principi; Cristina Palmero; Julio C. S. Jacques Junior; Sergio Escalera |
| Title |
On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
12 |
Issue |
3 |
Pages |
607-621 |
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| Abstract |
Personality perception is implicitly biased due to many subjective factors, such as cultural, social, contextual, gender and appearance. Approaches developed for automatic personality perception are not expected to predict the real personality of the target, but the personality external observers attributed to it. Hence, they have to deal with human bias, inherently transferred to the training data. However, bias analysis in personality computing is an almost unexplored area. In this work, we study different possible sources of bias affecting personality perception, including emotions from facial expressions, attractiveness, age, gender, and ethnicity, as well as their influence on prediction ability for apparent personality estimation. To this end, we propose a multi-modal deep neural network that combines raw audio and visual information alongside predictions of attribute-specific models to regress apparent personality. We also analyse spatio-temporal aggregation schemes and the effect of different time intervals on first impressions. We base our study on the ChaLearn First Impressions dataset, consisting of one-person conversational videos. Our model shows state-of-the-art results regressing apparent personality based on the Big-Five model. Furthermore, given the interpretability nature of our network design, we provide an incremental analysis on the impact of each possible source of bias on final network predictions. |
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1 July-Sept. 2021 |
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HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ PPJ2019 |
Serial |
3312 |
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Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier |
| Title |
Modeling, Recognizing, and Explaining Apparent Personality from Videos |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
13 |
Issue |
2 |
Pages |
894-911 |
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Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future. |
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1 April-June 2022 |
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HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ EKS2022 |
Serial |
3406 |
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| Author |
Fatemeh Noroozi; Ciprian Corneanu; Dorota Kamińska; Tomasz Sapiński; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
Survey on Emotional Body Gesture Recognition |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
12 |
Issue |
2 |
Pages |
505 - 523 |
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| Abstract |
Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as “body language” and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations. |
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HUPBA; no proj |
Approved |
no |
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Admin @ si @ NCK2021 |
Serial |
3657 |
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Kaustubh Kulkarni; Ciprian Corneanu; Ikechukwu Ofodile; Sergio Escalera; Xavier Baro; Sylwia Hyniewska; Juri Allik; Gholamreza Anbarjafari |
| Title |
Automatic Recognition of Facial Displays of Unfelt Emotions |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
12 |
Issue |
2 |
Pages |
377 - 390 |
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| Abstract |
Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average, it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase. |
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HUPBA; no proj |
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no |
| Call Number |
Admin @ si @ KCO2021 |
Serial |
3658 |
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Julio C. S. Jacques Junior; Yagmur Gucluturk; Marc Perez; Umut Guçlu; Carlos Andujar; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Marcel A. J. van Gerven; Rob van Lier; Sergio Escalera |
| Title |
First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
13 |
Issue |
1 |
Pages |
75-95 |
| Keywords |
Personality computing; first impressions; person perception; big-five; subjective bias; computer vision; machine learning; nonverbal signals; facial expression; gesture; speech analysis; multi-modal recognition |
| Abstract |
Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed. |
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1 Jan.-March 2022 |
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HuPBA |
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no |
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Admin @ si @ JGP2022 |
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3724 |
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| Author |
Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti |
| Title |
Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences |
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Journal Article |
| Year |
2011 |
Publication |
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control |
Abbreviated Journal |
T-UFFC |
| Volume |
58 |
Issue |
1 |
Pages |
60-72 |
| Keywords |
3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging |
| Abstract |
Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals. |
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0885-3010 |
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IAM;ADAS |
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no |
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IAM @ iam @ HGG2011 |
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1546 |
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David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich |
| Title |
Traffic sign recognition for computer vision project-based learning |
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Journal Article |
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2013 |
Publication |
IEEE Transactions on Education |
Abbreviated Journal |
T-EDUC |
| Volume |
56 |
Issue |
3 |
Pages |
364-371 |
| Keywords |
traffic signs |
| Abstract |
This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback. |
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0018-9359 |
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ADAS; CIC |
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no |
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Admin @ si @ GSL2013; ADAS @ adas @ |
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2160 |
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Mikhail Mozerov; Fei Yang; Joost Van de Weijer |
| Title |
Sparse Data Interpolation Using the Geodesic Distance Affinity Space |
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Journal Article |
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2019 |
Publication |
IEEE Signal Processing Letters |
Abbreviated Journal |
SPL |
| Volume |
26 |
Issue |
6 |
Pages |
943 - 947 |
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In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique. |
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LAMP; 600.120 |
Approved |
no |
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Admin @ si @ MYW2019 |
Serial |
3261 |
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| Author |
Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov |
| Title |
Variable Rate Deep Image Compression with Modulated Autoencoder |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Signal Processing Letters |
Abbreviated Journal |
SPL |
| Volume |
27 |
Issue |
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Pages |
331-335 |
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| Abstract |
Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters. |
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LAMP; ADAS; 600.141; 600.120; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ YHW2020 |
Serial |
3346 |
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
| Title |
Sparse representation over learned dictionary for symbol recognition |
Type |
Journal Article |
| Year |
2016 |
Publication |
Signal Processing |
Abbreviated Journal |
SP |
| Volume |
125 |
Issue |
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Pages |
36-47 |
| Keywords |
Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
| Abstract |
In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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| Notes |
DAG; 600.061; 600.077 |
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no |
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Admin @ si @ DTR2016 |
Serial |
2946 |
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