Efficient algorithms exist for finding optimal strategies in extensive-form games. However human scale problems, such as poker, are typically so large that computation of these strategies remain infeasible with current technology. State space abstraction techniques allow us to derive a smaller abstract game, in which an optimal strategy can be computed and then used in the real game. This thesis introduces state of the art abstraction techniques. Most of these techniques do not deal with public information. We present a new automatic public state space abstraction technique. We examine the quality of this technique in the domain of poker. Our experimental results show that the new technique brings significant performance improvement. Powered by TCPDF (www.tcpdf.org)

Recent advances in telecommunications, collaborated with the development of image and video processing and acquisition devices has lead to a spectacular growth of the amount of the visual content data stored, transmitted and exchanged over Internet. Within this context, elaborating efficient tools to access, browse and retrieve video content has become a crucial challenge. In Chapter 2 we introduce and validate a novel shot boundary detection algorithm able to identify abrupt and gradual transitions. The technique is based on an enhanced graph partition model, combined with a multi-resolution analysis and a non-linear filtering operation. The global computational complexity is reduced by implementing a two-pass approach strategy. In Chapter 3 the video abstraction problem is considered. In our case, we have developed a keyframe representation system that extracts a variable number of images from each detected shot, depending on the visual content variation. The Chapter 4 deals with the issue of high level semantic segmentation into scenes. Here, a novel scene/DVD chapter detection method is introduced and validated. Spatio-temporal coherent shots are clustered into the same scene based on a set of temporal constraints, adaptive thresholds and neutralized shots. Chapter 5 considers the issue of object detection and segmentation. Here we introduce a novel spatio-temporal visual saliency system based on: region contrast, interest points correspondence, geometric transforms, motion classes' estimation and regions temporal consistency. The proposed technique is extended on 3D videos by representing the stereoscopic perception as a 2D video and its associated depth

Les progrès récents en matière de télécommunications, collaboré avec le développement des dispositifs d'acquisition d’images et de vidéos a conduit à une croissance spectaculaire de la quantité des données vidéo stockées, transmises et échangées sur l’Internet. Dans ce contexte, l'élaboration d'outils efficaces pour accéder aux éléments d’information présents dans le contenu vidéo est devenue un enjeu crucial. Dans le Chapitre 2 nous introduisons un nouvel algorithme pour la détection de changement de plans vidéo. La technique est basée sur la partition des graphes combinée avec une analyse multi-résolution et d'une opération de filtrage non-linéaire. La complexité globale de calcul est réduite par l’application d'une stratégie deux passes. Dans le Chapitre 3 le problème d’abstraction automatique est considéré. Dans notre cas, nous avons adopté un système de représentation image-clés qui extrait un nombre variable d'images de chaque plan vidéo détecté, en fonction de la variation du contenu visuel. Le Chapitre 4 traite la segmentation de haut niveau sémantique. En exploitant l'observation que les plans vidéo appartenant à la même scène ont les mêmes caractéristiques visuelles, nous introduisons un nouvel algorithme de regroupement avec contraintes temporelles, qui utilise le seuillage adaptatif et les plans vidéo neutralisés. Dans le Chapitre 5 nous abordons le thème de détection d’objets vidéo saillants. Dans ce contexte, nous avons introduit une nouvelle approche pour modéliser l'attention spatio-temporelle utilisant : la correspondance entre les points d'intérêt, les transformations géométriques et l’estimation des classes de mouvement / Recent advances in telecommunications, collaborated with the development of image and video processing and acquisition devices has lead to a spectacular growth of the amount of the visual content data stored, transmitted and exchanged over Internet. Within this context, elaborating efficient tools to access, browse and retrieve video content has become a crucial challenge. In Chapter 2 we introduce and validate a novel shot boundary detection algorithm able to identify abrupt and gradual transitions. The technique is based on an enhanced graph partition model, combined with a multi-resolution analysis and a non-linear filtering operation. The global computational complexity is reduced by implementing a two-pass approach strategy. In Chapter 3 the video abstraction problem is considered. In our case, we have developed a keyframe representation system that extracts a variable number of images from each detected shot, depending on the visual content variation. The Chapter 4 deals with the issue of high level semantic segmentation into scenes. Here, a novel scene/DVD chapter detection method is introduced and validated. Spatio-temporal coherent shots are clustered into the same scene based on a set of temporal constraints, adaptive thresholds and neutralized shots. Chapter 5 considers the issue of object detection and segmentation. Here we introduce a novel spatio-temporal visual saliency system based on: region contrast, interest points correspondence, geometric transforms, motion classes’ estimation and regions temporal consistency. The proposed technique is extended on 3D videos by representing the stereoscopic perception as a 2D video and its associated depth