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Author David Sanchez-Mendoza; David Masip; Agata Lapedriza edit   file
doi  openurl
  Title Emotion recognition from mid-level features Type Journal Article
  Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 67 Issue Part 1 Pages 66–74  
  Keywords Facial expression; Emotion recognition; Action units; Computer vision  
  Abstract In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier B.V. Place of Publication Editor  
  Language Summary Language Original Title (down)  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0167-8655 ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number Admin @ si @ SML2015 Serial 2746  
Permanent link to this record
 

 
Author Gloria Fernandez Esparrach; Jorge Bernal; Maria Lopez Ceron; Henry Cordova; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; F. Javier Sanchez edit   pdf
doi  openurl
  Title Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps Type Journal Article
  Year 2016 Publication Endoscopy Abbreviated Journal END  
  Volume 48 Issue 9 Pages 837-842  
  Keywords  
  Abstract Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection.
Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence.
Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively.
Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice.
 
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  Language Summary Language Original Title (down)  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MV; Approved no  
  Call Number Admin @ si @FBL2016 Serial 2778  
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Author Santiago Segui; Michal Drozdzal; Guillem Pascual; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria edit   pdf
url  openurl
  Title 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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  Publisher Place of Publication Editor  
  Language Summary Language Original Title (down)  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes OR; MILAB;MV; Approved no  
  Call Number Admin @ si @ SDP2016 Serial 2836  
Permanent link to this record
 

 
Author Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton edit  doi
openurl 
  Title 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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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA; ISE;MV; Approved no  
  Call Number Admin @ si @ Serial 2851  
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Author David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville edit   pdf
url  openurl
  Title A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Type Journal Article
  Year 2017 Publication Journal of Healthcare Engineering Abbreviated Journal JHCE  
  Volume Issue Pages 2040-2295  
  Keywords Colonoscopy images; Deep Learning; Semantic Segmentation  
  Abstract Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.  
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  Language Summary Language Original Title (down)  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 Approved no  
  Call Number VBS2017b Serial 2940  
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