Pyramide Adaptative et Graphe Sémantique : Un processus de segmentation dirigé par la connaissance

National audienceCet article présente une méthode permettant d’intégrer àla fois une approche syntaxique et une approchesémantique dans un processus de segmentationautomatique. Cette intégration exploite les potentialitésoffertes par les représentations utilisant le formalisme desgraphes. La méthode repose sur le contrôle de lapertinence d’un critère de fusion utilisé dans unepyramide adaptative en mettant en correspondance lasegmentation obtenue et un graphe sémantique décrivantles objets recherchés. Cette mise en correspondance estréalisée par vérification de la consistance d’arc d’ungraphe sémantique défini a priori avec deux niveaux decontraintes. L’effectivité de cette approche a étéexpérimentée sur des images synthétisées et des imagesréelles

GRAPH CONSISTENCY CHECKING: A TOOL TO CHECK THE SEMANTIC CONSISTENCY OF A SEGMENTATION

International audienceThis paper describes an automatic process to check the semantic consistency of a segmentation.This process is made possible through the formalism of graphs. In this article we propose toapply this process to the checking of the relevancy of merging criteria used in an adaptivepyramid by matching the obtained segmentation with a conceptual graph describing theobjects under consideration. This matching is performed by using a discrete relaxation method.It checks the arc-consistency with bilevel constraints of the chosen semantic graph. The efficiencyof this approach is illustrated on synthetic and real images

Pyramide Adaptative et Graphe Sémantique : Un processus de segmentation dirigé par la connaissance

National audienceCet article présente une méthode permettant d’intégrer àla fois une approche syntaxique et une approchesémantique dans un processus de segmentationautomatique. Cette intégration exploite les potentialitésoffertes par les représentations utilisant le formalisme desgraphes. La méthode repose sur le contrôle de lapertinence d’un critère de fusion utilisé dans unepyramide adaptative en mettant en correspondance lasegmentation obtenue et un graphe sémantique décrivantles objets recherchés. Cette mise en correspondance estréalisée par vérification de la consistance d’arc d’ungraphe sémantique défini a priori avec deux niveaux decontraintes. L’effectivité de cette approche a étéexpérimentée sur des images synthétisées et des imagesréelles

Adaptative pyramid and semantic graph: knowledge driven segmentation

International audienceA method allowing to integrate syntactic and semantic approaches inan automatic segmentation process is described. This integration is possiblethanks to the formalism of graphs. The proposed method checks the relevancyof merging criteria used in an adaptive pyramid by matching the obtained seg-mentation with a semantic graph describing the objects that we look for. Thismatching is performed by checking the arc-consistency with bilevel constraintsof the chosen semantic graph. The validity of this approach is experimented onsynthetic and real images

Adaptative pyramid and semantic graph: knowledge driven segmentation

International audienceA method allowing to integrate syntactic and semantic approaches inan automatic segmentation process is described. This integration is possiblethanks to the formalism of graphs. The proposed method checks the relevancyof merging criteria used in an adaptive pyramid by matching the obtained seg-mentation with a semantic graph describing the objects that we look for. Thismatching is performed by checking the arc-consistency with bilevel constraintsof the chosen semantic graph. The validity of this approach is experimented onsynthetic and real images

Adaptative pyramid and semantic graph: knowledge driven segmentation

International audienceA method allowing to integrate syntactic and semantic approaches inan automatic segmentation process is described. This integration is possiblethanks to the formalism of graphs. The proposed method checks the relevancyof merging criteria used in an adaptive pyramid by matching the obtained seg-mentation with a semantic graph describing the objects that we look for. Thismatching is performed by checking the arc-consistency with bilevel constraintsof the chosen semantic graph. The validity of this approach is experimented onsynthetic and real images

Beyond Regularity: Equational Tree Automata for Associative and Commutative Theories

Abstract. A new tree automata framework, called equational tree au-tomata, is presented. In the newly introduced setting, congruence closures of recognizable tree languages are recognizable. Furthermore, we prove that in certain useful cases, recognizable tree languages are closed un-der union and intersection. To compare with early related work, e.g. [7], we discuss the relationship between linear bounded automata and equa-tional tree automata. As a consequence, we obtain some (un)decidability results. We further present a hierarchy of 4 classes of tree languages