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Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera |
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BoVDW: Bag-of-Visual-and-Depth-Words for Gesture Recognition |
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2012 |
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21st International Conference on Pattern Recognition |
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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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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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HuPBA;MV |
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Admin @ si @ HBP2012 |
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2122 |
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Javier Vazquez; Robert Benavente; Maria Vanrell |
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Naming constraints constancy |
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2012 |
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2nd Joint AVA / BMVA Meeting on Biological and Machine Vision |
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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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CIC |
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Admin @ si @ VBV2012 |
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2131 |
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Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco |
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An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction |
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2012 |
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2nd Joint AVA / BMVA Meeting on Biological and Machine Vision |
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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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Admin @ si @ OPD2012a |
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2132 |
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Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Text/graphic separation using a sparse representation with multi-learned dictionaries |
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2012 |
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21st International Conference on Pattern Recognition |
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Graphics Recognition; Layout Analysis; Document Understandin |
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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. |
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Tsukuba |
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DAG |
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Admin @ si @ DTR2012a |
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2135 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Noise suppression over bi-level graphical documents using a sparse representation |
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2012 |
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Colloque International Francophone sur l'Écrit et le Document |
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Bordeaux |
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CIFED |
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DAG |
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Admin @ si @ DTR2012b |
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2136 |
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Adriana Romero; Simeon Petkov; Carlo Gatta; M.Sabate; Petia Radeva |
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Efficient automatic segmentation of vessels |
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2012 |
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16th Conference on Medical Image Understanding and Analysis |
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Swansea, United Kingdom |
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MIUA |
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MILAB |
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Admin @ si @ |
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2137 |
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Miguel Oliveira; V.Santos; Angel Sappa |
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Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition |
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2012 |
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IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles |
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Algarve; Portugal |
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ADAS |
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Admin @ si @ OSS2012c |
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2159 |
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Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Stable Salient Shapes |
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2012 |
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International Conference on Digital Image Computing: Techniques and Applications |
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DICTA |
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MILAB |
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Admin @ si @ MCG2012b |
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2166 |
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Rui Hua; Oriol Pujol; Francesco Ciompi; Marina Alberti; Simone Balocco; J. Mauri; Petia Radeva |
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Stent Strut Detection by Classifying a Wide Set of IVUS Features |
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2012 |
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Computed Assisted Stenting Workshop |
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Nice, France |
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STENT |
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MILAB;HuPBA |
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Admin @ si @ HPC2012 |
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2169 |
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Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
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Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization |
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2014 |
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25th British Machine Vision Conference |
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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. |
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Nottingham; UK; September 2014 |
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BMVC |
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LAMP; CIC; 600.074; 600.079 |
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Admin @ si @ RWB2014 |
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2508 |
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Mirko Arnold; Stephan Ameling; Anarta Ghosh; Gerard Lacey |
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Quality Improvement of Endoscopy Videos |
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2011 |
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Proceedings of the 8th IASTED International Conference on Biomedical Engineering |
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fernando @ fernando @ |
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Stefan Ameling; Stephan Wirth; Dietrich Paulus; Gerard Lacey; Fernando Vilariño |
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Texture-based Polyp Detection in Colonoscopy |
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2009 |
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Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN |
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MV;SIAI |
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fernando @ fernando @ |
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Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
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Speech balloon contour classification in comics |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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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. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.056 |
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Admin @ si @ RKB2013 |
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2429 |
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Fernando Vilariño; Gerard Lacey |
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QUALITY ASSESSMENT IN COLONOSCOPY New challenges through computer vision-based systems |
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2009 |
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in Proc. 3rd International Conference on Biomedical Electronics and Devices |
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MV;SIAI |
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fernando @ fernando @ |
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2430 |
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Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Incremental Domain Adaptation of Deformable Part-based Models |
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2014 |
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25th British Machine Vision Conference |
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Pedestrian Detection; Part-based models; Domain Adaptation |
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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. |
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Nottingham; uk; September 2014 |
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BMVA Press |
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Editor |
Valstar, Michel and French, Andrew and Pridmore, Tony |
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BMVC |
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Notes |
ADAS; 600.057; 600.054; 600.076 |
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Call Number |
XRV2014c; ADAS @ adas @ xrv2014c |
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2455 |
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