Case-based object recognition

Abstract

Model-based object recognition is a well-known task in Computer Vision. Usually, one object that can be generalized by a model should be detected in an image based on this model. Biomedical applications have the special quality that one object can have a great variation in appearance. Therefore the appearance of this object cannot be generalized by one model. A set of cases of the appearance of this object (sometimes 50 cases or more) is necessary to detect this object in an image. The recognition method is rather a case-based object recognition than a model-based object recognition. Case-based object recognition is a challenging task. It puts special requirements to the similarity measure and needs a matching algorithm that can work fast on a large number of cases. In this paper we describe the chosen case representation, the similarity measure and the recent matching algorithm. Finally, we give results on the performance of the system. © Springer-Verlag 2004.