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Author Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza de Luna; Joaquin Salas edit   pdf
doi  openurl
  Title (down) Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices Type Journal Article
  Year 2016 Publication Neurocomputing Abbreviated Journal NEUCOM  
  Volume 175 Issue B Pages 866–876  
  Keywords Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices  
  Abstract During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset.  
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  Notes OR; 600.072; 600.068;MV Approved no  
  Call Number Admin @ si @ TRM2016 Serial 2721  
Permanent link to this record
 

 
Author Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton edit  doi
openurl 
  Title (down) Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis Type Journal Article
  Year 2016 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 28 Issue Pages 1489 - 1491  
  Keywords  
  Abstract The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others.  
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  Notes HuPBA; ISE;MV; Approved no  
  Call Number Admin @ si @ Serial 2851  
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Author Jorge Bernal; Aymeric Histace; Marc Masana; Quentin Angermann; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Maroua Hammami; Ana Garcia Rodriguez; Henry Cordova; Olivier Romain; Gloria Fernandez Esparrach; Xavier Dray; F. Javier Sanchez edit   pdf
doi  openurl
  Title (down) GTCreator: a flexible annotation tool for image-based datasets Type Journal Article
  Year 2019 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR  
  Volume 14 Issue 2 Pages 191–201  
  Keywords Annotation tool; Validation Framework; Benchmark; Colonoscopy; Evaluation  
  Abstract Abstract Purpose: Methodology evaluation for decision support systems for health is a time consuming-task. To assess performance of polyp detection
methods in colonoscopy videos, clinicians have to deal with the annotation
of thousands of images. Current existing tools could be improved in terms of
exibility and ease of use. Methods:We introduce GTCreator, a exible annotation tool for providing image and text annotations to image-based datasets.
It keeps the main basic functionalities of other similar tools while extending
other capabilities such as allowing multiple annotators to work simultaneously
on the same task or enhanced dataset browsing and easy annotation transfer aiming to speed up annotation processes in large datasets. Results: The
comparison with other similar tools shows that GTCreator allows to obtain
fast and precise annotation of image datasets, being the only one which offers
full annotation editing and browsing capabilites. Conclusions: Our proposed
annotation tool has been proven to be efficient for large image dataset annota-
tion, as well as showing potential of use in other stages of method evaluation
such as experimental setup or results analysis.
 
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  Notes MV; 600.096; 600.109; 600.119; 601.305 Approved no  
  Call Number Admin @ si @ BHM2019 Serial 3163  
Permanent link to this record
 

 
Author Oriol Pujol; David Masip edit  doi
openurl 
  Title (down) Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary Type Journal Article
  Year 2009 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 31 Issue 6 Pages 1140–1146  
  Keywords  
  Abstract This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention  
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  Notes OR;HuPBA;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ PuM2009 Serial 1252  
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Author Santiago Segui; Michal Drozdzal; Guillem Pascual; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria edit   pdf
url  openurl
  Title (down) Generic Feature Learning for Wireless Capsule Endoscopy Analysis Type Journal Article
  Year 2016 Publication Computers in Biology and Medicine Abbreviated Journal CBM  
  Volume 79 Issue Pages 163-172  
  Keywords Wireless capsule endoscopy; Deep learning; Feature learning; Motility analysis  
  Abstract The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).  
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  Notes OR; MILAB;MV; Approved no  
  Call Number Admin @ si @ SDP2016 Serial 2836  
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