A Similarity-Based Object Recognition Algorithm
Petra Perner, Silke Jänichen · 2013
Model-based object recognition is a challenging task especially if the objects of interest have a great natural variance. Such applications cannot be solved by only one general case, a case base is necessary to detect all objects with a sufficiently high accuracy. A flexible matching strategy is required that selects the most similar case out of the case base and fits it to an unseen object in a restricted distance transformation. In this paper we present our matching algorithm that establish point correspondences between a shape case and the found edges extracted from new images. A positive match will be found if the similarity measure holds