Ontology Based Complex Object Recognition

International audienceThis paper presents a new approach for object categorization involving the following aspects of cognitive vision: learning, recognition and knowledge representation.A major element of our approach is a visual concept ontology composed of several types of concepts (spatial concepts and relations, color concepts and texture concepts). Visual concepts contained in this ontology can be seen as an intermediate layer between domain knowledge and image processing procedures. Machine learning techniques are used to solve the symbol grounding problem (i.e. linking meaningfully symbols to sensory information). This paper shows how a new object categorization system is set up by a knowledge acquisition and learning phase and then used by an object categorization phase

An autonomous method of optimization targeting for an object detection system based on responsability analysis

Object extraction systems performances are not homogeneous over different corpora because objects can take many different aspects within such sets. An adaptation of these systems is thus required in order to maintain equal performances over every kind of object the system may be applied on. Focusing on the issue of parameters optimization, a method has been developed to restrict optimization to parameters of operators which compose the system, responsible for the different categories of errors produced by the system. Two stages are involved in our method. The first one is dedicated to the analysis of the system performances and leads to the extraction of the different error categories already mentionned. The second one relates to the analysis of the behavior of the different operators, leading to extract a single operator responsible for each error category. Experiments have been carried out over a video text detection system.Les systèmes d'extraction d'objets sont mis à mal par la diversité de ces derniers. Leur adaptation est donc nécessaire pour maintenir des performances équivalentes quelle que soit la nature des objets sur lesquels ceux-ci sont appliqués. S'attachant plus particulièrement, dans l'optique de cette adaptation, à la tâche d'optimisation du paramétrage de ces systèmes, nous proposons dans cet article une méthode originale de ciblage de l'optimisation aux seuls paramètres des opérateurs du système estimés responsables des différentes catégories d'erreurs produites par le système. Cette méthode s'appuie alors sur deux analyses distinctes. La première porte sur les performances du système considéré et permet d'extraire les différentes catégories d'erreur déjà mentionnées. La seconde concerne le fonctionnement des différents opérateurs composant le système et donne lieu à la détermination d'un opérateur responsable pour chaque catégorie d'erreur. Une application de cette méthodologie à un système de détection de texte est par ailleurs détaillée

INVICON: A Toolkit for Knowledge-Based Control of Vision Systems

To perform as desired in a dynamic environment a vision system must adapt to a variety of operating conditions by selecting vision modules, tuning their parameters, and controlling image acquisition. Knowledge-based (KB) controller-agents that reason over explicitly represented knowledge and interact with their environment can be used for this task; however, the lack of a unifying methodology and development tools makes KB controllers difficult to create, maintain, and reuse. This paper presents the IN-VICON toolkit, based on the IndiGolog agent programming language with elements from control theory. It provides a basic methodology, a vision module declaration template, a suite of control components, and support tools for KB controller development. We have evaluated INVICON in two case studies that involved controlling vision-based pose estimation systems. The case studies show that INVICON reduces the effort needed to build KB controllers for challenging domains and improves their flexibility and robustness. 1