View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure

Authors:
Daniel P. Huttenlocher;Ryan H. Lilien;Clark F. Olson
Affiliations:
Cornell Univ., Ithaca, NY;Dartmouth Univ., Hanover, NH;NASA JPL, Pasadena, CA
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1999

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Abstract

View-based recognition methods, such as those using eigenspace techniques, have been successful for a number of recognition tasks. Such approaches, however, are somewhat limited in their ability to recognize objects that are partly hidden from view or occur against cluttered backgrounds. In order to address these limitations, we have developed a view matching technique based on an eigenspace approximation to the generalized Hausdorff measure. This method achieves the compact storage and fast indexing that are the main advantages of eigenspace view matching techniques, while also being tolerant of partial occlusion and background clutter. The method applies to binary feature maps, such as intensity edges, rather than directly to intensity images.