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Author Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera edit   pdf
doi  openurl
  Title Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History Type Journal Article
  Year 2016 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 28 Issue 8 Pages 1548-1568  
  Keywords Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal  
  Abstract Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.  
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  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ COC2016 Serial 2718  
Permanent link to this record
 

 
Author Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza edit   pdf
doi  openurl
  Title Evolving weighting schemes for the Bag of Visual Words Type Journal Article
  Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications  
  Volume 28 Issue 5 Pages 925–939  
  Keywords Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision  
  Abstract The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.
 
  Address  
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  Publisher Place of Publication Editor Springer  
  Language Summary Language Original Title  
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  Area Expedition Conference  
  Notes HUPBA;MV; no menciona;OR;MILAB Approved no  
  Call Number Admin @ si @ EPE2017 Serial 2743  
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Author Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria edit   pdf
doi  openurl
  Title Automatic garment retexturing based on infrared information Type Journal Article
  Year 2016 Publication Computers & Graphics Abbreviated Journal CG  
  Volume 59 Issue Pages 28-38  
  Keywords Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading  
  Abstract This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms.  
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  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN (up) ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ ADT2016 Serial 2759  
Permanent link to this record
 

 
Author 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  
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  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ EAG2016 Serial 2764  
Permanent link to this record
 

 
Author Cristina Palmero; Jordi Esquirol; Vanessa Bayo; Miquel Angel Cos; Pouya Ahmadmonfared; Joan Salabert; David Sanchez; Sergio Escalera edit   pdf
doi  openurl
  Title Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis Type Journal Article
  Year 2017 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 122 Issue 2 Pages 212–227  
  Keywords Sleep system recommendation; RGB-Depth data Pressure imaging; Anthropometric landmark extraction; Multi-part human body segmentation  
  Abstract This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures.  
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  Area Expedition Conference  
  Notes HuPBA;MILAB; 303.100 Approved no  
  Call Number Admin @ si @ PEB2017 Serial 2765  
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