A flexible similarity measure for 3D shapes recognition

This paper is devoted to presenting a new strategy for 3D objects recognition using a flexible similarity measure based on the recent modeling wave (MW) topology in spherical models. MW topology allows us to establish an n-connectivity relationship in 3D objects modeling meshes. Using the complete object model, a study on considering different partial information of the model has been carried out to recognize an object. For this, we have introduced a new feature called cone-curvature (CC), which originates from the MW concept. CC gives an extended geometrical surroundings knowledge for every node of the mesh model and allows us to define a robust and adaptable similarity measure between objects for a specific model database. The defined similarity metric has been successfully tested in our lab using range data of a wide variety of 3D shapes. Finally, we show the applicability of our method presenting experimentation for recognition on noise and occlusion conditions in complex scenes.

1 Introduction

The similarity concept is, in a general sense, an ambiguous and relative concept for humans. This is due to the fact that men have not got an extrinsic similarity measure. Intuitively, we say that two objects are similar through the external appearance or some local/global features, but we can not measure how much the first object looks like the second one. Nevertheless, we can always maintain whether or not they are similar. Even for this case, different results depending on the perception of each person would be obtained. In this field, Santini and Jain [1] have carried out a study on similarity measures for selecting images based on characteristics of human similarity measurement. Anyway, the similarity is usually determined as a distance between some extracted features or a vector that combines these [2].