Issue No. 11 - November (2004 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.94
Antonio Ad? , IEEE
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.
Computer vision, feature measurement, object recognition, similarity measures, pattern recognition.
A. Ad? and M. Ad?, "A Flexible Similarity Measure for 3D Shapes Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 1507-1520, 2004.