Segmentation de scènes extérieures à partir d'ensembles d'étiquettes à granularité et sémantique variables

International audienceIn this work, we present an approach that leverages multiple datasets annotated using different classes (different labelsets) to improve the classification accuracy on each individual dataset. We focus on semantic full scene labeling of outdoor scenes. To achieve our goal, we use the KITTI dataset as it illustrates very well the focus of our paper : it has been sparsely labeled by multiple research groups over the past few years but the semantics and the granularity of the labels differ from one set to another. We propose a method to train deep convolutional networks using multiple datasets with potentially inconsistent labelsets and a selective loss function to train it with all the available labeled data while being reliant to inconsistent labelings. Experiments done on all the KITTI dataset's labeled subsets show that our approach consistently improves the classification accuracy by exploiting the correlations across data-sets both at the feature level and at the label level.Ce papier présente une approche permettant d'utiliser plusieurs bases de données annotées avec différents ensembles d'étiquettes pour améliorer la précision d'un classifieur entrainé sur une tâche de segmentation sémantique de scènes extérieures. Dans ce contexte, la base de données KITTI nous fournit un cas d'utilisation particulièrement pertinent : des sous-ensembles distincts de cette base ont été annotés par plusieurs équipes en utilisant des étiquettes différentes pour chaque sous-ensemble. Notre méthode permet d'entraîner un réseau de neurones convolutionnel (CNN) en utilisant plusieurs bases de données avec des étiquettes possiblement incohérentes. Nous présentons une fonction de perte sélective pour entrainer ce réseau et plusieurs approches de fusion permettant d'exploiter les corrélations entre les différents ensembles d'étiquettes. Le réseau utilise ainsi toutes les données disponibles pour améliorer les performances de classification sur chaque ensemble. Les expériences faites sur les différents sous-ensembles de la base de données KITTI montrent comment chaque proposition contribue à améliorer le classifieur

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Stéganographie d'images basées sur le théorème chinois des restes

- Dans ce papier nous nous intéressons au problème de cacher dans une image, moyennant une seule opération, de multiple messages provenant de multiples utilisateurs. L'idée est d'utiliser le théorème chinois des restes. Sur le plan théorique, il est établi qu'un nombre illimité de messages peuvent être insérée dans l'image elle-même. Une des limites de l'approche vient du fait que ces multiples messages ne doivent être ni détectables, ni visibles. Par construction, la première condition est naturellement satisfaite, par contre toute la difficulté consiste à satisfaire la seconde condition. Un atout de cette approche est que chaque utilisateur peut disposer d'une clé différente pour détecter une des marques superposée à l'image

A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves

International audienceDue to its high spatial and spectral information content, hyperspectral imaging opens up new possibilities for a better understanding of data and scenes in a wide variety of applications. An essential part of this process of understanding is the classification part. However, the high spatial and spectral resolution also leads to enormous amounts of data. The effective handling and use of such datasets for classification requires processing steps (dimensionality reduction through feature selection or feature extraction) that are not always goal-oriented. In this article, a new general classification approach is presented that uses the geometric shape of spectral signatures instead of purely statistical methods. In contrast to classical classification approaches (e.g., SVM, KNN), not only are reflectance values taken into account, but also parameters such as curvature points, curvature values, and the curvature behavior of spectral signatures are used to develop shape-describing rules in order to use them for classification by a rule-based procedure with IF-THEN queries. The flexibility and efficiency of the methodology are demonstrated on datasets from two different application domains and lead to convincing results with good performance

Deep Fisher Score Representation via Sparse Coding

International audienc

Protecting Documents Using Printed Anticopy Elements

International audienceThis chapter provides a brief overview of the different approaches to document authentication, before focusing on the different approaches to protection using elements sensitive to the variability inherent in copying systems. Improvements in the performance and availability of document printing and scanning systems have contributed to an upsurge in counterfeit document production. Printed document authentication processes rely on an initial registration step, carried out when the document is printed, before any verification can be carried out. A print test shapes (PTS) is a continuous element considered to be a binary object for analysis, containing the discriminating characteristics of a print. Copy-sensitive graphical codes (CSGCs) are discret elements that are designed to be difficult to duplicate from an authentic print. The performance of any document protection system based on the use of anti-copy elements, whether in the form of a PTS or CSGC, is dependent on in-depth knowledge of the print-scan chain involved

Color-based and rotation invariant self-similarities

International audienceOne big challenge in computer vision is to extract robust and discriminative local descriptors. For many applications such as object tracking, image classification or image matching, there exist appearance-based descriptors such as SIFT or learned CNN-features that provide very good results. But for some other applications such as multimodal image comparison (infra-red versus color, color versus depth, ...) these descriptors failed and people resort to using the spatial distribution of self-similarities. The idea is to inform about the similarities between local regions in an image rather than the appearances of these regions at the pixel level. Nevertheless, the classical self-similarities are not invariant to rotation in the image space, so that two rotated versions of a local patch are not considered as similar and we think that many discriminative information is lost because of this weakness. In this paper, we present a method to extract rotation-invariant self similarities. In this aim, we propose to compare color descriptors of the local regions rather than the local regions themselves. Furthermore, since this comparison informs us about the relative orientations of the two local regions, we incorporate this information in the final image descriptor in order to increase the discriminative power of the system. We show that the self similarities extracted by this way are very discriminative