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Author Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera edit   pdf
isbn  openurl
  Title BoVDW: Bag-of-Visual-and-Depth-Words for Gesture Recognition Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
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  Abstract We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition pipeline, where Dynamic Time Warping (DTW) algorithm is used to perform prior segmentation of gestures. Results of the method in public data sets, within our gesture recognition pipeline, show better performance in comparison to a standard BoVW model.  
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  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes HuPBA;MV Approved no  
  Call Number Admin @ si @ HBP2012 Serial 2122  
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Author Javier Vazquez; Robert Benavente; Maria Vanrell edit   pdf
url  openurl
  Title Naming constraints constancy Type Conference Article
  Year 2012 Publication 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision Abbreviated Journal  
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  Abstract Different studies have shown that languages from industrialized cultures
share a set of 11 basic colour terms: red, green, blue, yellow, pink, purple, brown, orange, black, white, and grey (Berlin & Kay, 1969, Basic Color Terms, University of California Press)( Kay & Regier, 2003, PNAS, 100, 9085-9089). Some of these studies have also reported the best representatives or focal values of each colour (Boynton and Olson, 1990, Vision Res. 30,1311–1317), (Sturges and Whitfield, 1995, CRA, 20:6, 364–376). Some further studies have provided us with fuzzy datasets for color naming by asking human observers to rate colours in terms of membership values (Benavente -et al-, 2006, CRA. 31:1, 48–56,). Recently, a computational model based on these human ratings has been developed (Benavente -et al-, 2008, JOSA-A, 25:10, 2582-2593). This computational model follows a fuzzy approach to assign a colour name to a particular RGB value. For example, a pixel with a value (255,0,0) will be named 'red' with membership 1, while a cyan pixel with a RGB value of (0, 200, 200) will be considered to be 0.5 green and 0.5 blue. In this work, we show how this colour naming paradigm can be applied to different computer vision tasks. In particular, we report results in colour constancy (Vazquez-Corral -et al-, 2012, IEEE TIP, in press) showing that the classical constraints on either illumination or surface reflectance can be substituted by
the statistical properties encoded in the colour names. [Supported by projects TIN2010-21771-C02-1, CSD2007-00018].
 
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  Area Expedition Conference AV A  
  Notes CIC Approved no  
  Call Number Admin @ si @ VBV2012 Serial 2131  
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Author Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco edit   pdf
url  openurl
  Title An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction Type Conference Article
  Year 2012 Publication 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision Abbreviated Journal  
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  Abstract Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. From a purely computational perspective, we built a low-level computational model (CIWaM) of early sensory processing based on multi-resolution wavelets with the aim of replicating brightness and colour (Otazu et al., 2010, Journal of Vision, 10(12):5) induction effects. Furthermore, we successfully used the CIWaM architecture to define a computational saliency model (Murray et al, 2011, CVPR, 433-440; Vanrell et al, submitted to AVA/BMVA'12). From a biological perspective, neurophysiological evidence suggests that perceived brightness information may be explicitly represented in V1. In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (Li, 1999, Network:Comput. Neural Syst., 10, 187-212) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as visual saliency, which share with brightness induction the relevant effect of contextual influences (the ones modelled by CIWaM). In the proposed model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition taken from the computational model (CIWaM).
This model successfully accounts for well known pyschophysical effects (among them: the White's and modied White's effects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction effects) for static contexts and also for brigthness induction in dynamic contexts defined by modulating the luminance of surrounding areas. From a methodological point of view, we conclude that the results obtained by the computational model (CIWaM) are compatible with the ones obtained by the neurodynamical model proposed here.
 
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  Area Expedition Conference AV A  
  Notes CIC Approved no  
  Call Number Admin @ si @ OPD2012a Serial 2132  
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Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades edit   pdf
url  openurl
  Title Text/graphic separation using a sparse representation with multi-learned dictionaries Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords Graphics Recognition; Layout Analysis; Document Understandin  
  Abstract In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds.  
  Address Tsukuba  
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  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ DTR2012a Serial 2135  
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Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades edit   pdf
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  Title Noise suppression over bi-level graphical documents using a sparse representation Type Conference Article
  Year 2012 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal  
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  Address Bordeaux  
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  Area Expedition Conference CIFED  
  Notes DAG Approved no  
  Call Number Admin @ si @ DTR2012b Serial 2136  
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Author Adriana Romero; Simeon Petkov; Carlo Gatta; M.Sabate; Petia Radeva edit   pdf
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  Title Efficient automatic segmentation of vessels Type Conference Article
  Year 2012 Publication 16th Conference on Medical Image Understanding and Analysis Abbreviated Journal  
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  Address Swansea, United Kingdom  
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  Area Expedition Conference MIUA  
  Notes MILAB Approved no  
  Call Number Admin @ si @ Serial 2137  
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Author Miguel Oliveira; V.Santos; Angel Sappa edit  openurl
  Title Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition Type Conference Article
  Year 2012 Publication IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles Abbreviated Journal  
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  Address Algarve; Portugal  
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  Area Expedition Conference PPNIV  
  Notes ADAS Approved no  
  Call Number Admin @ si @ OSS2012c Serial 2159  
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Author Pedro Martins; Paulo Carvalho; Carlo Gatta edit   pdf
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  Title Stable Salient Shapes Type Conference Article
  Year 2012 Publication International Conference on Digital Image Computing: Techniques and Applications Abbreviated Journal  
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  Area Expedition Conference DICTA  
  Notes MILAB Approved no  
  Call Number Admin @ si @ MCG2012b Serial 2166  
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Author Rui Hua; Oriol Pujol; Francesco Ciompi; Marina Alberti; Simone Balocco; J. Mauri; Petia Radeva edit   pdf
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  Title Stent Strut Detection by Classifying a Wide Set of IVUS Features Type Conference Article
  Year 2012 Publication Computed Assisted Stenting Workshop Abbreviated Journal  
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  Address Nice, France  
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  Area Expedition Conference STENT  
  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ HPC2012 Serial 2169  
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Author Adria Ruiz; Joost Van de Weijer; Xavier Binefa edit   pdf
url  openurl
  Title Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization Type Conference Article
  Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal  
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  Abstract We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection.  
  Address Nottingham; UK; September 2014  
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  Area Expedition Conference BMVC  
  Notes LAMP; CIC; 600.074; 600.079 Approved no  
  Call Number Admin @ si @ RWB2014 Serial 2508  
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Author Mirko Arnold; Stephan Ameling; Anarta Ghosh; Gerard Lacey edit  doi
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  Title Quality Improvement of Endoscopy Videos Type Conference Article
  Year 2011 Publication Proceedings of the 8th IASTED International Conference on Biomedical Engineering Abbreviated Journal  
  Volume 723 Issue Pages (up)  
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  Area 800 Expedition Conference  
  Notes MV Approved no  
  Call Number fernando @ fernando @ Serial 2426  
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Author Stefan Ameling; Stephan Wirth; Dietrich Paulus; Gerard Lacey; Fernando Vilariño edit  doi
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  Title Texture-based Polyp Detection in Colonoscopy Type Conference Article
  Year 2009 Publication Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN Abbreviated Journal  
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  Notes MV;SIAI Approved no  
  Call Number fernando @ fernando @ Serial 2428  
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Author Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier edit  openurl
  Title Speech balloon contour classification in comics Type Conference Article
  Year 2013 Publication 10th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
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  Abstract Comic books digitization combined with subsequent comic book understanding create a variety of new applications, including mobile reading and data mining. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. In this work we detail a novel approach for classifying speech balloon in scanned comics book pages based on their contour time series.  
  Address Bethlehem; PA; USA; August 2013  
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  Area Expedition Conference GREC  
  Notes DAG; 600.056 Approved no  
  Call Number Admin @ si @ RKB2013 Serial 2429  
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Author Fernando Vilariño; Gerard Lacey edit  openurl
  Title QUALITY ASSESSMENT IN COLONOSCOPY New challenges through computer vision-based systems Type Conference Article
  Year 2009 Publication in Proc. 3rd International Conference on Biomedical Electronics and Devices Abbreviated Journal  
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  Area 800 Expedition Conference  
  Notes MV;SIAI Approved no  
  Call Number fernando @ fernando @ Serial 2430  
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Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Incremental Domain Adaptation of Deformable Part-based Models Type Conference Article
  Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal  
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  Keywords Pedestrian Detection; Part-based models; Domain Adaptation  
  Abstract Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data.
 
  Address Nottingham; uk; September 2014  
  Corporate Author Thesis  
  Publisher BMVA Press Place of Publication Editor Valstar, Michel and French, Andrew and Pridmore, Tony  
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  Area Expedition Conference BMVC  
  Notes ADAS; 600.057; 600.054; 600.076 Approved no  
  Call Number XRV2014c; ADAS @ adas @ xrv2014c Serial 2455  
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