Multi-pass Feedback Control for Object Recognition

In order to locate interesting areas of an image we describe a system for focus of attention; this is based on feedback strategies combining low-level features, and a high-level object model to recognise the object and to direct the search for missing information. We aim to improve on established single-pass hypothesis generation and veri cation approaches by applying our complex feedback strategies to recognise generic classes of objects. By using a complex feedback strategy we produce optimal sets of low level features and reduce the number of hypotheses generated. The system can extract simple and complex objects in a scale and rotation independent manner where the objects may be partially occluded. The method is illustrated for simple cubic objects and the results are expected to be applied for a mobile robot application. A n de situer les r egions d'int erêt d'une image, nous d ecrivons un syst eme de centre d'attention bas e sur des strat egies de retour de donn ees. Cellesci comprennent plusieurs caract eristiques de basniveau ainsi q'un mod ele de haut-niveau lequel reconnaît l'objet et dirige l'op eration de recherche des donn ees manquantes. Notre objectif est d'am eliorer l'approche de g en eration et de v eri cation des hypoth eses en utilisant nos strat egies de retour de donn ees a n d'identi er des cat egories g en eriques d'objets. Grâce a ces strat egies nous obtiendrons un ensemble optimal de caract eristiques de bas-niveau et nous r eduirons le nombre d'hypoth eses g en er ees. Le syst eme est capable de reconnaître des objets simples ou complexes ind ependamment de l' echelle, de la rotation ou bien même de l'occlusion partielle d'un objet. Nous d emontrerons cette m ethode pour de simples objets cubiques. Celle-l a sera utilis ee Now at Emrad Ltd., Catteshall Lane, Godalming, Surrey GU7 1NG, UK pour une application de robot mobile.

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