Shape similarity and visual parts

Abstract

Human perception of shape is based on visual parts of objects to a point that a single, significant visual part is sufficient to recognize the whole object. For example, if you see a hand in the door, you expect a human behind the door. Therefore, a cognitively motivated shape similarity measure for recognition applications should be based on visual parts. This cognitive assumption leads to two related problems of scale selection and subpart selection. To find a given query part Q as part of an object C, Q needs to have a correct size with regards to C (scale selection). Assuming that the correct size is selected, the part Q must be compared to all possible subparts of C (subpart selection). For global, contour-based similarity measures, scaling the whole contour curves of both objects to the same length usually solves the problem of scale selection. Although this is not an optimal solution, it works if the whole contour curves are 'sufficiently' similar. Subpart selection problem does not occur in the implementation of global similarity measures. In this paper we present a shape similarity system that is based on correspondence of visual parts, and apply it to robot localization and mapping. This is a particularly interesting application, since the scale selection problem does not occur here and visual parts can be obtained in a very simple way. Therefore, only the problem of subpart selection needs to be solved. Our solution to this problem is based on a contour based shape similarity measure supplemented by a structural arrangement information of visual parts. © Springer-Verlag Berlin Heidelberg 2003.