Integration of Geometric and Non-Geometric Attributes for Fast Object Recognition

Both man-made and natural objects are described by both their geometric shapes and by their non-geometric attributes such as color. The objective of the proposed research is to create a system which integrates geometric and non-geometric attribute information for fast 3D model-based object recognition. Hashing is employed in a hypothesize and verify approach to the 3D model-based object recognition problem. Viewpoint independent attributes are used in the hypothesis generation stage to eliminate model objects from consideration during hypothesis formation. Utilizing more than one attribute in the proposed hashing scheme helps to ensure a reduction in the actual execution time for object recognition over a larger number of model bases. A nice feature of the system is that new object attributes can be added with relative ease. Issues concerning the ranking of attributes by their distinctiveness with respect to the objects in the model base are discussed. Current work on the use of an information theoretic measure in the design of an optimal multiple attribute hash table is presented.