. Harris, Comme on peut le voir sur cet exemple, les points de Laplace-Harris sont moins nombreux sur les bords et dans les zones bruitées très contrastées Ceci permet une mise en correspondance plus robuste, car l'appariement de tels points d'intérêt est peu informative. Cependant, toutes ces méthodes sont très sensibles au bruit et au changement de contraste En particulier, on observe qu'aucun point d'intérêt n'est détecté sur la robe dans l'ombre (figure B.6(a)), malgré la présence de nombreuses jonctions Une perspective intéressante est donnée par les approches de détection a contrario qui ont été proposées pour les détections de jonctions en L [Cao04] (figure B.6(e)) ou en T [Bél06] (figure B.6(f)) : des points sont détectés dans les zones peu contrastées mais très structurées, tout en limitant le nombre de fausses détections dans les zones bruitées. Cette question de l'invariance des détecteurs de points d'intérêt est primordiale car elle conditionne l'ensemble des performances d'un système de reconnaissance d'objets. La figure B.7(a) illustre ce problème dans le cas de la recherche d'un logo dans une publicité. Sur chaque bouteille, le logo apparaît deux fois : en grand sur le bas de la bouteille, et en haut sur le col. En raison de la qualité médiocre de l'image (faible résolution, artefacts de compression, faible contraste sur certains objets), très peu de points d'intérêt sont détectés dans la seconde image. Les mises en correspondance de ces points sont données en figure B.7(b) En conséquence, tous les logos à petite échelle ou peu contrastés ne peuvent pas être détectés (figure B.7(c)), en raison de l'absence ou d'un nombre insuffisant de points d'intérêt Une solution proposée dans, Nous avons également souligné au chapitre 4 le problème des répétitions de points d'intérêt à différentes échelles, comme on peut le voir sur les images B.6(b) et B.6(c). Ce phénomène se produit notamment pour les structures correspondant à des jonctions de bords

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