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Author 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  
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  Area Expedition Conference  
  Notes MILAB; OR;HuPBA;MV Approved no  
  Call Number Admin @ si @ EBV2012 Serial 1885  
Permanent link to this record
 

 
Author David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson edit   pdf
doi  openurl
  Title Automated Prediction of Preferences Using Facial Expressions Type Journal Article
  Year 2014 Publication PloS one Abbreviated Journal Plos  
  Volume 9 Issue 2 Pages e87434  
  Keywords  
  Abstract We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available.  
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  Notes OR;MV Approved no  
  Call Number Admin @ si @ MNT2014 Serial 2453  
Permanent link to this record
 

 
Author Laura Igual; Agata Lapedriza; Ricard Borras edit   pdf
doi  openurl
  Title Robust Gait-Based Gender Classification using Depth Cameras Type Journal Article
  Year 2013 Publication EURASIP Journal on Advances in Signal Processing Abbreviated Journal EURASIPJ  
  Volume 37 Issue 1 Pages 72-80  
  Keywords  
  Abstract This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section.  
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  Area Expedition Conference  
  Notes MILAB; OR;MV Approved no  
  Call Number Admin @ si @ ILB2013 Serial 2144  
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Author Onur Ferhat; Fernando Vilariño edit   pdf
doi  openurl
  Title Low Cost Eye Tracking: The Current Panorama Type Journal Article
  Year 2016 Publication Computational Intelligence and Neuroscience Abbreviated Journal CIN  
  Volume Issue Pages Article ID 8680541  
  Keywords  
  Abstract Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools.  
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  Area Expedition Conference  
  Notes MV; 605.103; 600.047; 600.097;SIAI Approved no  
  Call Number Admin @ si @ FeV2016 Serial 2744  
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Author Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey edit  doi
openurl 
  Title Automatic segmentation and inpainting of specular highlights for endoscopic imaging Type Journal Article
  Year 2010 Publication EURASIP Journal on Image and Video Processing Abbreviated Journal EURASIP JIVP  
  Volume 2010 Issue 9 Pages  
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  ISSN ISBN Medium  
  Area 800 Expedition Conference  
  Notes MV Approved no  
  Call Number fernando @ fernando @ Serial 2423  
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