Completed Local Structure Patterns on Three Orthogonal Planes for Dynamic Texture Recognition

International audienceDynamic texture (DT) is a challenging problem in computer vision because of the chaotic motion of textures. We address in this paper a new dynamic texture operator by considering local structure patterns (LSP) and completed local binary patterns (CLBP) for static images in three orthogonal planes to capture spatial-temporal texture structures. Since the typical operator of local binary patterns (LBP), which uses center pixel for thresholding, has some limitations such as sensitivity to noise and near uniform regions, the proposed approach can deal with these drawbacks by using global and local texture information for adaptive thresholding and CLBP for exploiting complementary texture information in three orthogonal planes. Evaluations on different datasets of dynamic textures (UCLA, DynTex, DynTex++) show that our proposal significantly outper-forms recent results in the state-of-the-art approaches

Directional Dense-Trajectory-based Patterns for Dynamic Texture Recognition

International audienceRepresentation of dynamic textures (DTs), well-known as a sequence of moving textures, is a challenging problem in video analysis due to disorientation of motion features. Analyzing DTs to make them "under-standable" plays an important role in different applications of computer vision. In this paper, an efficient approach for DT description is proposed by addressing the following novel concepts. First, beneficial properties of dense trajectories are exploited for the first time to efficiently describe DTs instead of the whole video. Second, two substantial extensions of Local Vector Pattern operator are introduced to form a completed model which is based on complemented components to enhance its performance in encoding directional features of motion points in a trajectory. Finally, we present a new framework, called Directional Dense Trajectory Patterns , which takes advantage of directional beams of dense trajectories along with spatio-temporal features of their motion points in order to construct dense-trajectory-based descriptors with more robustness. Evaluations of DT recognition on different benchmark datasets (i.e., UCLA, DynTex, and DynTex++) have verified the interest of our proposal

Volumes of Blurred-Invariant Gaussians for Dynamic Texture Classification

International audienceAn effective model, which jointly captures shape and motion cues, for dynamic texture (DT) description is introduced by taking into account advantages of volumes of blurred-invariant features in three main following stages. First, a 3-dimensional Gaussian kernel is used to form smoothed sequences that allow to deal with well-known limitations of local encoding such as near uniform regions and sensitivity to noise. Second , a receptive volume of the Difference of Gaussians (DoG) is figured out to mitigate the negative impacts of environmental and illumination changes which are major challenges in DT understanding. Finally, a local encoding operator is addressed to construct a discriminative descriptor of enhancing patterns extracted from the filtered volumes. Evaluations on benchmark datasets (i.e., UCLA, DynTex, and DynTex++) for issue of DT classification have positively validated our crucial contributions

Utilisation des courbes de niveaux et estimation robuste pour la détection de contour en imagerie médicale tomodensitomètrique par contours déformables bidirectionnelles

Dans le traitement et l'exploration de certaines affections liées à un organe, une représentation globale à trois dimensions de ce dernier s'avère fort utile. Cet article traite de la partie segmentation et extraction des contours successifs liés à un même organe sur une séquence d'images. Dans le cas de l'aorte, les subdivisions en branches compliquent considérablement le problème en y introduisant des changements radicaux de topologie au niveau des coupes. Basé sur le principe des contours actifs, l'algorithme que nous présentons utilise un modèle d'image particulier et un estimateur robuste qui lui est associé. En utilisant la notion de courbes de niveaux et une expression originale de la vitesse d'évolution, cela permet un ajustement bidirectionnel rapide ainsi que la prise en compte des changements de topologie

Modélisation des images et segmentation par contours déformables

Cet article traite de la modélisation et de la segmentation automatique des images par contours actifs déformables. Nous proposons une généralisation de la méthode présentée par Amadieu et al [3] et nous obtenons une modélisation des images. Un domaine d'application est le suivi d'organes sur une séquence d'images médicales formée par les coupes successives de tomodensitométrie X. L'application de cette méthode de modélisation permet la détection des objets dont les niveaux de gris ne se situent pas vers le maximum de l'histogramme, mais sur des niveaux intermédiaires, tout en conservant l'évolution bidirectionnelle du contour et la possibilité de traiter des changements de topologie des objets

Efficient algorithm for computation of the second-order moment of the subpixel-edge position

National audienceno abstrac

Guiding Text Image Keypoints Extraction through Layout Analysis

International audienc

Estimation du champ de déplacement dense sur une séquence échocardiographique

National audienceno abstrac

Method for segmenting a source image

no abstrac