Search results for "visual saliency"
showing 3 items of 23 documents
Spatio-Temporal Saliency Detection in Dynamic Scenes using Local Binary Patterns
2014
International audience; Visual saliency detection is an important step in many computer vision applications, since it reduces further processing steps to regions of interest. Saliency detection in still images is a well-studied topic. However, videos scenes contain more information than static images, and this additional temporal information is an important aspect of human perception. Therefore, it is necessary to include motion information in order to obtain spatio-temporal saliency map for a dynamic scene. In this paper, we introduce a new spatio-temporal saliency detection method for dynamic scenes based on dynamic textures computed with local binary patterns. In particular, we extract l…
Contributions à l'analyse et à l'interprétation des images : Extraction et représentation de caractéristiques
2016
Ce mémoire, rédigé en vue de l'obtention de l'Habilitation à Diriger des Recherches (HDR), offre un aperçu des travaux de recherche et d’encadrement que j’ai pu mener depuis l'obtention de mon doctorat. Il montre la diversité des champs d’application et de recherche (en vision et en imagerie médicale) que j’ai pu couvrir , ainsi que mon implication dans l’encadrement doctoral.Mes activités de recherche se divisent en deux grandes parties. D'une part, l'analyse de scènes dynamiques, à savoir la détection de regions d'intérêt dans des séquences d'images, pour réduire la taile des données à traiter, et la détection et le suivi d'objets mobiles à l'aide de caméras de diverses natures (perspecti…
Visual saliency by keypoints distribution analysis
2011
In this paper we introduce a new method for Visual Saliency detection. The goal of our method is to emphasize regions that show rare visual aspects in comparison with those showing frequent ones. We propose a bottom up approach that performs a new technique based on low level image features (texture) analysis. More precisely, we use SIFT Density Maps (SDM), to study the distribution of keypoints into the image with different scales of observation, and its relationship with real fixation points. The hypothesis is that the image regions that show a larger distance from the mode (most frequent value) of the keypoints distribution over all the image are the same that better capture our visual a…