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Proceedings Paper

Integration of geometric and nongeometric attributes for fast object-recognition
Author(s): Lynne L. Grewe; Avinash C. Kak
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Paper Abstract

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 3-D model-based object recognition. Hashing is employed in a hypothesis and verify approach to the 3-D 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.

Paper Details

Date Published: 11 March 1993
PDF: 16 pages
Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993); doi: 10.1117/12.141778
Show Author Affiliations
Lynne L. Grewe, Purdue Univ. (United States)
Avinash C. Kak, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 1964:
Applications of Artificial Intelligence 1993: Machine Vision and Robotics
Kim L. Boyer; Louise Stark, Editor(s)

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