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            <title xml:lang="en">Information management in the first stages of computer vision</title>
            <title xml:lang="fr">Gestion des informations dans les premieres etapes de la vision par ordinateur</title>
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              <p>In computer vision, the way the problem is addressed is very important. As regards the detection of the first visual features, like edges or regions, the theoretical approach is difficult because the goal is not clearly defined. Our approach consists in building step by step a satisfactory solution, essentially taking into account the way information has to be processed. We propose simple principles to get an efficient information management and powerful decisions strategies. An incremental control structure seems to be a very efficient way to apply our principles and to prepare an heuristic- based approach. For example, by translating our expertise in an algorithm, we have built a new edge detector with adaptive thresholding, which results appear to us better than those of classic edge detectors for a great number of images. This edge detector is unsupervised and detect "step edges" as well as "line edges". We also propose a cooperation between our edge detector and a region growing process, that agregates pixels one by one to build regions. Compared to other cooperating algorithms, our control structure is much more flexible, the information management allowing to get, at the right time, at the right place, the complementary information needed to take the right decision. However, we still have to make progress, using more information, in particular concerning the shape of the regions being built. Our conclusion is that segmentation suffers from a too restrictive modelisation. To carry on making progress in that domain, we may try to address problems differently.</p>
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              <p>Dans le cadre de la vision par ordinateur, la facon d'aborder les problemes est delicate. En ce qui concerne la detection des premiers indices visuels, tels que les contours ou les regions, l'approche theorique est difficile, car le but n'est pas clairement defini. Notre approche consiste a avancer pas a pas vers une solution satisfaisante en nous preoccupant essentiellement de la gestion des informations pour traduire une expertise humaine. Nous proposons des principes simples pour une gestion efficace des informations, tels que l'accumulation des informations, leur complementarite, leur "objectivite" et leur "liberte" de mouvement. Ces principes mettent en avant les problemes de controle, d'adaptation au contexte local et l'organisation de l'algorithme. Une structure de controle incrementale nous semble particulierement efficace pour mettre en oeuvre nos principes et preparer une strategie heuristique. Ainsi, en "copiant" notre expertise, nous avons realise un nouveau detecteur de contours avec seuillage adaptatif dont les resultats nous paraissent meilleurs que ceux obtenus avec des detecteurs classiques sur de nombreuses images. Ce detecteur ne necessite l'utilisation d'aucun seuil et detecte a la fois les contours de type "marche" et les contours de type "trait". Nous proposons egalement un algorithme de cooperation entre un processus de croissance de region par agregation de pixels et notre detecteur de contours. Comparee a d'autres techniques de cooperation, notre structure de controle est beaucoup plus souple, la gestion des informations ayant ete etudiee pour obtenir au moment opportun, a l'endroit desire, les informations complementaires permettant un meilleur controle de la decision. Il reste toutefois a parfaire notre cooperation en exploitant plus d'informations, en particulier au niveau de la forme des regions generees. Notre conclusion est que la segmentation souffre peut-etre d'une modelisation trop restrictive. Pour progresser dans ce domaine, il faut sans doute aborder les problemes differemment.</p>
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