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Author Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera edit   pdf
openurl 
  Title Deteccion automatica de la dominancia en conversaciones diadicas Type Journal Article
  Year 2010 Publication Escritos de Psicologia Abbreviated Journal EP  
  Volume (up) 3 Issue 2 Pages 41–45  
  Keywords Dominance detection; Non-verbal communication; Visual features  
  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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1989-3809 ISBN Medium  
  Area Expedition Conference  
  Notes HUPBA; OR; MILAB;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ EMV2010 Serial 1315  
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Author Zhen Xu; Sergio Escalera; Adrien Pavao; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao; Isabelle Guyon edit  doi
openurl 
  Title Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform Type Journal Article
  Year 2022 Publication Patterns Abbreviated Journal PATTERNS  
  Volume (up) 3 Issue 7 Pages 100543  
  Keywords Machine learning; data science; benchmark platform; reproducibility; competitions  
  Abstract Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning.  
  Address June 24, 2022  
  Corporate Author Thesis  
  Publisher Science Direct Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ XEP2022 Serial 3764  
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Author Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title Texture Segmentation by Statistical Deformable Models Type Journal
  Year 2004 Publication International Journal of Image and Graphics Abbreviated Journal IJIG  
  Volume (up) 4 Issue 3 Pages 433-452  
  Keywords Texture segmentation, parametric active contours, statistic snakes  
  Abstract Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PuR2004a Serial 505  
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Author Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera edit   pdf
doi  openurl
  Title Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization Type Journal Article
  Year 2012 Publication Journal of Ambient Intelligence and Smart Environments Abbreviated Journal JAISE  
  Volume (up) 4 Issue 6 Pages 535-546  
  Keywords Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation  
  Abstract We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches.  
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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1876-1364 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ HZM2012a Serial 2006  
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Author Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera edit  url
openurl 
  Title Análisis de la expresión oral y gestual en proyectos fin de carrera vía un sistema de visión artificial Type Journal Article
  Year 2011 Publication ReVisión Abbreviated Journal  
  Volume (up) 4 Issue 1 Pages  
  Keywords  
  Abstract La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1989-1199 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA; MILAB;MV;OR Approved no  
  Call Number Admin @ si @ PGB2011d Serial 2514  
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