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Author (down) Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta edit   pdf
doi  openurl
  Title Diaphragm border detection in coronary X-ray angiographies: New method and applications Type Journal Article
  Year 2014 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG  
  Volume 38 Issue 4 Pages 296-305  
  Keywords  
  Abstract X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation.  
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  Area Expedition Conference  
  Notes MILAB; LAMP; 600.079 Approved no  
  Call Number Admin @ si @ PCR2014 Serial 2468  
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Author (down) Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu edit   pdf
doi  openurl
  Title Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction Type Journal Article
  Year 2012 Publication Sensors Abbreviated Journal SENS  
  Volume 12 Issue 2 Pages 1702-1719  
  Keywords  
  Abstract IF=1.77 (2010)
Social interactions are a very important component in peopleís 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 Timesí Blogging Heads opinion blog.
The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî 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.
 
  Address  
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  Publisher Molecular Diversity Preservation International 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 MILAB; OR;HuPBA;MV Approved no  
  Call Number Admin @ si @ EBV2012 Serial 1885  
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Author (down) Sergio Escalera; Vassilis Athitsos; Isabelle Guyon edit  url
openurl 
  Title Challenges in multimodal gesture recognition Type Journal Article
  Year 2016 Publication Journal of Machine Learning Research Abbreviated Journal JMLR  
  Volume 17 Issue Pages 1-54  
  Keywords Gesture Recognition; Time Series Analysis; Multimodal Data Analysis; Computer Vision; Pattern Recognition; Wearable sensors; Infrared Cameras; KinectTM  
  Abstract This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectTMrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands
of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Zhuowen Tu  
  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 @ EAG2016 Serial 2764  
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Author (down) 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 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.  
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  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 (down) Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera edit  doi
openurl 
  Title Automatic Detection of Dominance and Expected Interest Type Journal Article
  Year 2010 Publication EURASIP Journal on Advances in Signal Processing Abbreviated Journal EURASIPJ  
  Volume Issue Pages 12  
  Keywords  
  Abstract Article ID 491819
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.
 
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1110-8657 ISBN Medium  
  Area Expedition Conference  
  Notes OR;MILAB;HUPBA;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2010d Serial 1283  
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