TREAT: Terse Rapid Edge-Anchored Tracklets

Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets. It harnesses moving edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications

Gaussian normalization: handling burstiness in visual data

This paper addresses histogram burstiness, defined as the tendency of histograms to feature peaks out of pro- portion with their general distribution. After highlighting the impact of this growing issue on computer vision prob- lems and the need to preserve the distribution informa- tion, we introduce a new normalization based on a Gaus- sian fit with a pre-defined variance for each datum that suppresses burst without adversely affecting the distribu- tion. Experimental results on four public datasets show that our normalization scheme provides a staggering per- formance boost compared to other normalizations, even al- lowing Gaussian-normalized Bag-of-Words to perform sim- ilarly to intra-normalized Fisher vectors

LBP Channels for Pedestrian Detection

International audienceThis paper introduces a new channel descriptor for pedestrian detection. This type of descriptor usually selects a set of one-valued filters within the enormous set of all possible filters for improved efficiency. The main claim underpinning this paper is that the recent works on channel-based features restrict the filter space search, therefore bringing along the obsolescence of one-valued filter representation. To prove our claim, we introduce a 12-valued filter representation based on local binary patterns. Indeed, various improvements now allow for this texture feature to provide a very discriminative, yet compact descriptor. Filter selection boasting new combination restrictions as well as a reverse selection process are also presented to choose the best filters. experiments on the INRIA and Caltech-USA datasets validate the approach

Optimisation de Base de Donnée pour la Détection de Piétons temps-réel

International audienceThis paper tackles data selection for training set generation in the context of nearreal-time pedestrian detection through the introduction of a training methodology: FairTrain.After highlighting the impact of poorly chosen data on detector performance, we will introduce anew data selection technique utilizing the expectation-maximization algorithm for data weighting.FairTrain also features a version of the cascade-of-rejectors enhanced with data selection principles.Experiments on the INRIA and PETS2009 datasets prove that, when ne trained, a simple HoG-based detector can perform on par with most of its near real-time competitors.Ce document traite de la sélection de données pour la génération de l’ensembled’entraînement dans le contexte de la détection des piétons en temps-réel grâce a l’introductiond’une méthodologie: FairTrain. Après avoir souligné l’impact des données mal choisies sur lesperformances des détecteurs, nous allons présenter une nouvelle technique de sélection de donnéespondéré par l’algorithme d’expectation-maximization. FairTrain propose également une versionde cascade-de-rejecteurs améliorée avec des principes de sélection de données. Les expériencessur les bases de données INRIA et Caltech prouvent que, lorsqu’ils sont bien formés, un simpledétecteur basé sur des HoGs fonctionne aussi bien que ses concurrents temps-réel

Action recognition in video using a spatial-temporal graph-based feature representation

We propose a video graph based human action recognition framework. Given an input video sequence, we extract spatio-temporal local features and construct a video graph to incorporate appearance and motion constraints to reflect the spatio-temporal dependencies among features. them. In particular, we extend a popular dbscan density-based clustering algorithm to form an intuitive video graph. During training, we estimate a linear SVM classifier using the standard Bag-of-words method. During classification, we apply Graph-Cut optimization to find the most frequent action label in the constructed graph and assign this label to the test video sequence. The proposed approach achieves stateof-the-art performance with standard human action recognition benchmarks, namely KTH and UCF-sports datasets and competitive results for the Hollywood (HOHA) dataset

Analysis of 339 pregnancies in 181 women with 13 different forms of inherited thrombocytopenia

65Pregnancy in women with inherited thrombocytopenias is a major matter of concern as both the mothers and the newborns are potentially at risk of bleeding. However, medical management of this condition cannot be based on evidence because of the lack of consistent information in the literature. To advance knowledge on this matter, we performed a multicentric, retrospective study evaluating 339 pregnancies in 181 women with 13 different forms of inherited thrombocytopenia. Neither the degree of thrombocytopenia nor the severity of bleeding tendency worsened during pregnancy and the course of pregnancy did not differ from that of healthy subjects in terms of miscarriages, fetal bleeding and pre-term births. The degree of thrombocytopenia in the babies was similar to that in the mother. Only 7 of 156 affected newborns had delivery-related bleeding, but 2 of them died of cerebral hemorrhage. The frequency of delivery-related maternal bleeding ranged from 6.8% to 14.2% depending on the definition of abnormal blood loss, suggesting that the risk of abnormal blood loss was increased with respect to the general population. However, no mother died or had to undergo hysterectomy to arrest bleeding. The search for parameters predicting delivery-related bleeding in the mother suggested that hemorrhages requiring blood transfusion were more frequent in women with history of severe bleedings before pregnancy and with platelet count at delivery below 50 × 10(9)/L.openopenPatrizia Noris; Nicole Schlegel; Catherine Klersy; Paula G. Heller; Elisa Civaschi; Nuria Pujol-Moix; Fabrizio Fabris; Remi Favier; Paolo Gresele; Véronique Latger-Cannard; Adam Cuker; Paquita Nurden; Andreas Greinacher; Marco Cattaneo; Erica De Candia; Alessandro Pecci; Marie-Françoise Hurtaud-Roux; Ana C. Glembotsky; Eduardo Muñiz-Diaz; Maria Luigia Randi; Nathalie Trillot; Loredana Bury; Thomas Lecompte; Caterina Marconi; Anna Savoia; Carlo L. Balduini; Sophie Bayart; Anne Bauters; Schéhérazade Benabdallah-Guedira; Françoise Boehlen; Jeanne-Yvonne Borg; Roberta Bottega; James Bussel; Daniela De Rocco; Emmanuel de Maistre; Michela Faleschini; Emanuela Falcinelli; Silvia Ferrari; Alina Ferster; Tiziana Fierro; Dominique Fleury; Pierre Fontana; Chloé James; Francois Lanza; Véronique Le Cam Duchez; Giuseppe Loffredo; Pamela Magini; Dominique Martin-Coignard; Fanny Menard; Sandra Mercier; Annamaria Mezzasoma; Pietro Minuz; Ilaria Nichele; Lucia D. Notarangelo; Tommaso Pippucci; Gian Marco Podda; Catherine Pouymayou; Agnes Rigouzzo; Bruno Royer; Pierre Sie; Virginie Siguret; Catherine Trichet; Alessandra Tucci; Béatrice Saposnik; Dino VeneriPatrizia, Noris; Nicole, Schlegel; Catherine, Klersy; Paula G., Heller; Elisa, Civaschi; Nuria Pujol, Moix; Fabrizio, Fabris; Remi, Favier; Paolo, Gresele; Véronique Latger, Cannard; Adam, Cuker; Paquita, Nurden; Andreas, Greinacher; Marco, Cattaneo; Erica De, Candia; Alessandro, Pecci; Marie Françoise Hurtaud, Roux; Ana C., Glembotsky; Eduardo Muñiz, Diaz; Maria Luigia, Randi; Nathalie, Trillot; Loredana, Bury; Thomas, Lecompte; Caterina, Marconi; Savoia, Anna; Carlo L., Balduini; Sophie, Bayart; Anne, Bauters; Schéhérazade Benabdallah, Guedira; Françoise, Boehlen; Jeanne Yvonne, Borg; Bottega, Roberta; James, Bussel; DE ROCCO, Daniela; Emmanuel de, Maistre; Faleschini, Michela; Emanuela, Falcinelli; Silvia, Ferrari; Alina, Ferster; Tiziana, Fierro; Dominique, Fleury; Pierre, Fontana; Chloé, James; Francois, Lanza; Véronique Le Cam, Duchez; Giuseppe, Loffredo; Pamela, Magini; Dominique Martin, Coignard; Fanny, Menard; Sandra, Mercier; Annamaria, Mezzasoma; Pietro, Minuz; Ilaria, Nichele; Lucia D., Notarangelo; Tommaso, Pippucci; Gian Marco, Podda; Catherine, Pouymayou; Agnes, Rigouzzo; Bruno, Royer; Pierre, Sie; Virginie, Siguret; Catherine, Trichet; Alessandra, Tucci; Béatrice, Saposnik; Dino, Vener

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TREAT: Terse Rapid Edge-Anchored Tracklets

Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets. It harnesses moving edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications