Noise Tolerant Descriptor for Texture Classification

International audienceAmong many texture descriptors, the LBP-based representation emerged as an attractive approach thanks to its low complexity and effectiveness. Many variants have been proposed to deal with several limitations of the basic approach like the small spatial support or the noise sensitivity. This paper presents a new method to construct an effective texture descriptor addressing those limitations by combining three features: (1) a circular average filter is applied before calculating the Complemented Local Binary Pattern (CLBP), (2) the histogram of CLBPs is calculated by weighting the contribution of every local pattern according to the gradient magnitude, and (3) the image features are calculated at different scales using a pyramidal framework. An efficient calculation of the pyramid using integral images, together with a simple construction of the multi-scale histogram based on concatenation, make the proposed approach both fast and memory efficient. Experimental results on different texture classification databases show the good results of the method, and its excellent noise robustness, compared to recent LBP-based methods

Statistical binary patterns for rotational invariant texture classification

International audienceA new texture representation framework called statistical binary patterns (SBP) is presented. It consists in applying rotation invariant local binary pattern operators (LBP riu2) to a series of moment images, defined by local statistics uniformly computed using a given spatial support. It can be seen as a generalisation of the commonly used complementation approach (CLBP), since it extends the local description not only to local contrast information, but to higher order local variations. In short, SBPs aim at expanding LBP self-similarity operator from the local gray level to the regional distribution level. Thanks to a richer local description, the SBPs have better discrimination power than other LBP variants. Furthermore, thanks to the regularisation effect of the statistical moments, the SBP descriptors show better noise robustness than classical CLBPs. The interest of the approach is validated through a large experimental study performed on five texture databases: KTH-TIPS, KTH-TIPS 2b, CUReT, UIUC and DTD. The results show that, for the four first datasets, the SBPs are comparable or outperform the recent state-of-the-art methods, even using small support for the LBP operator, and using limited size spatial support for the computation of the local statistics

Integrating Experimental Data Sets and Simulation Codes for Students into a MOOC on Aerial Robotics

International audienceThis paper addresses the integration of experimental data sets and simulation codes into a MOOC. Such additional material is both used in the lecture videos and proposed to the students as a complement to put into practice the theoretical notions presented. The chosen pedagogic approach hence relies on a tight integration between these elements and is illustrated through the example of DroMOOC, a MOOC on aerial robotics

Ground-plane classification for robot navigation: combining multiple cues toward a visual-based learning system

This paper describes a vision-based ground-plane classification system for autonomous indoor mobile-robot that takes advantage of the synergy in combining together multiple visual-cues. A priori knowledge of the environment is important in many biological systems, in parallel with their reactive systems. As such, a learning model approach is taken here for the classification of the ground/object space, initialised through a new Distributed-Fusion (D-Fusion) method that captures colour and textural data using Superpixels. A Markov Random Field (MRF) network is then used to classify, regularise, employ a priori constraints, and merge additional ground/object information provided by other visual cues (such as motion) to improve classification images. The developed system can classify indoor test-set ground-plane surfaces with an average true-positive to false-positive rate of 90.92% to 7.78% respectively on test-set data. The system has been designed in mind to fuse a variety of different visual-cues. Consequently it can be customised to fit different situations and/or sensory architectures accordingly

Dense Hough transforms on gray level images using multi-scale derivatives

Abstract. The Hough transform for detecting parameterised shapes in images is still today mostly applied on binary images of contours or connected sets, which implies pre-processing of the images that may be costly and fragile. However the simple estimation of the spatial derivatives provides in every pixel the local geometry that can be used for dense voting processes, directly applied on the gray scale image. For lines and circles, the local information even allows to perform a direct 1-to-1 projection from the image to the parameter space, which greatly accelerates the accumulation process. In this paper we advocate the use of direct detection on gray scale images by combining Hough transform and multi-scale derivatives. We present the algorithms and discuss their results in the case of analytical shapes for order one (lines), and two (circles), and then we present the generalised Hough transform based on quantised derivatives for detecting arbitrary (non-analytical) shapes.

OBJECT MODELLING, DETECTION AND LOCALISATION IN MOBILE VIDEO: A STATE-OF-THE-ART

This report is part of the state-of-the-art deliverable of the ITEA2 project SPY “Surveillance imProved System: Intelligent situation awareness”whose purpose is to develop new urban surveillance systems using video cameras embedded within mobile security vehicles. This report is dedicated to the problem of finding objects of interest in a video. “Object” is understood in its familiar (i.e. semantic) sense: e.g. car, tree, human, road... and the system is supposed to automatically find the location of such objects in the captured video. To be consistent with the project technological level, we shall exclude the “developmental” approaches, where the system does not know the objects in advance, and constructsincrementally its own internal representation. We then suppose that the system operates with a provided representation of the objects and its environment that has been constructed (learned) off-line, and that may evolve on-line. Such representation includes a set of object classes that the system is then expected to recognize and localise in every image, either by attributing accordingly a label to every location in the image (task referred to as “semantic segmentation”), or by localising –more or less precisely– instances of each class in the video and tagging every image accordingly (referred to as “semantic indexing”). We thus present a state-of-the-art of the video analysis methods for object and environment modelling and semantic indexing or segmentation with respect to the corresponding model

: UNIVERSITÉ PIERRE ET MARIE CURIE HABILITATION À DIRIGER DES RECHERCHES

Même s'il n'existe toujours pas de système visuel artificiel dont les performances soient comparables à notre vision biologique quant à sa diversité et ses capacités d'adaptation, les progrès réalisés au cours des dernières décennies dans les domaines du traitement et de l'interprétation automatique d'images et de vidéos sont considérables. De nombreux succès ont été obtenus dans des applications diverses et souvent inattendues. Le contexte actuel est celui d'un domaine fortement concurrentiel à la fois sur les plans académique et industriel. La puissance de calcul disponible sur n'importe quel processeur récent, et la multiplication des bibliothèques logicielles de traitement d'images permettent même aux amateurs de construire leur application à un niveau fonctionnel. Mais cette logique modulaire a ses limites, d'abord pour les applications embarquées qui exigent une optimisation des performances aussi globale que possible, mais aussi parce qu'il est indispensable de revisiter en permanence les briques existantes et d'en imaginer de nouvelles, surtout dans un contexte où l'évolution des architectures sur étagère ne se résume plus à une augmentation de la fréquence d'horloge et des tailles mémoire, mais affecte la nature du parallélisme, les hiérarchies mémoire, la topologie, etc. Ma recherche vise à améliorer les performances d'un Système de Vision en le considérant dans son ensemble, depuis le modèle de représentation, les algorithmes et les structures de données, jusqu'à l'implantation parallèle sur un système de Vision embarqué. Mon objectif est d'améliorer l'autonomie des Systèmes de Vision, aussi bien du point de vue énergétique (efficacité), que fonctionnel (robustesse). Mes contributions ont touché essentiellement au Traitement d'Images et à la Vision précoce, et se répartissent dans les trois thématiques suivantes :* Représentation et Traitement des Images : Je m'intéresse aux modèles de représentation de l'information visuelle~: géométriques, statistiques, discrets... ainsi qu'aux structures de données et aux algorithmes de traitement associés.* Analyse du Mouvement : Je recherche les algorithmes les plus efficaces pour extraire d'une vidéo les informations de mouvement les plus pertinentes du point de vue de la surveillance ou de la navigation.* Systèmes de Vision Embarqués : Je cherche à exploiter de façon optimale une architecture parallèle en adaptant les algorithmes de vision à la puissance de calcul et au flux de données disponibles