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Search Results: 1 - 10 of 10120 matches for " Sergio Escalera "
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Detection of Complex Salient Regions
Sergio Escalera,Oriol Pujol,Petia Radeva
EURASIP Journal on Advances in Signal Processing , 2008, DOI: 10.1155/2008/451389
Abstract: The goal of interest point detectors is to find, in an unsupervised way, keypoints easy to extract and at the same time robust to image transformations. We present a novel set of saliency features based on image singularities that takes into account the region content in terms of intensity and local structure. The region complexity is estimated by means of the entropy of the gray-level information; shape information is obtained by measuring the entropy of significant orientations. The regions are located in their representative scale and categorized by their complexity level. Thus, the regions are highly discriminable and less sensitive to confusion and false alarm than the traditional approaches. We compare the novel complex salient regions with the state-of-the-art keypoint detectors. The presented interest points show robustness to a wide set of image transformations and high repeatability as well as allow matching from different camera points of view. Besides, we show the temporal robustness of the novel salient regions in real video sequences, being potentially useful for matching, image retrieval, and object categorization problems.
Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction
Sergio Escalera,Xavier Baró,Jordi Vitrià,Petia Radeva,Bogdan Raducanu
Sensors , 2012, DOI: 10.3390/s120201702
Abstract: Social interactions are a very important component in people’s lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links’ weights are a measure of the “influence” a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.
AUTOMATIC DOMINANCE DETECTION IN DYADIC CONVERSATIONS (Detección automática de la dominancia en conversaciones diádicas)
Sergio Escalera,Rosa M. Martínez,Jordi Vitrià,Petia Radeva
Escritos de Psicología , 2010,
Abstract: Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correla tion among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers’ opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences.
Automatic Detection of Dominance and Expected Interest
Sergio Escalera,Oriol Pujol,Petia Radeva,Jordi Vitrià
EURASIP Journal on Advances in Signal Processing , 2010, DOI: 10.1155/2010/491819
Abstract: Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.
GrabCut-Based Human Segmentation in Video Sequences
Antonio Hernández-Vela,Miguel Reyes,Víctor Ponce,Sergio Escalera
Sensors , 2012, DOI: 10.3390/s121115376
Abstract: In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology.
Automatic dominance detection in dyadic conversations
Escalera,Sergio; Martínez,Rosa M.; Vitrià,Jordi; Radeva,Petia; Anguera,M. Teresa;
Escritos de Psicología (Internet) , 2010,
Abstract: dominance is referred to the level of influence that a person has in a conversation. dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. in this paper, we focus on the dominance detection of visual cues. we estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers' opinion. moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences.
Non-Verbal Communication Analysis in Victim-Offender Mediations
Víctor Ponce-López,Sergio Escalera,Marc Pérez,Oriol Janés,Xavier Baró
Computer Science , 2014,
Abstract: In this paper we present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. In particular, we propose the use of computer vision and social signal processing technologies in real scenarios of Victim-Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real world Victim-Offender Mediation sessions in Catalonia in collaboration with the regional government. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state-of-the-art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1-5] for the computed social signals.
Error-Correcting Factorization
Miguel Angel Bautista,Oriol Pujol,Fernando de la Torre,Sergio Escalera
Computer Science , 2015,
Abstract: Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches.
A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder
Laura Igual, Joan Soliva, Antonio Hernández-Vela, Sergio Escalera, Xavier Jiménez, Oscar Vilarroya, Petia Radeva
BioMedical Engineering OnLine , 2011, DOI: 10.1186/1475-925x-10-105
Abstract: We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure.We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis.CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.Studies of volumetric brain magnetic resonance imaging (MRI) show neuroanatomical abnormalities in pediatric attention-deficit/hyperactivity disorder (ADHD) [1-3]. ADHD is a developmental disorder characterized by inatten-tiveness, motor hyperactivity and impulsiveness, and it represents the most prevalent childhood psychiatric disorder. It is also estimated that half the children with ADHD will display the disorder in adulthood. As stated in several reviews and metanalyses, diminished right caudate
A Gesture Recognition System for Detecting Behavioral Patterns of ADHD
Miguel ángel Bautista,Antonio Hernández-Vela,Sergio Escalera,Laura Igual,Oriol Pujol,Josep Moya,Verónica Violant,María Teresa Anguera
Computer Science , 2014,
Abstract: We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.
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