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<p><b>Abstract</b>—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.</p>
Model-based recognition, Hausdorff matching, subspace methods, image matching.
Daniel P. Huttenlocher, Clark F. Olson, Ryan H. Lilien, "View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 951-955, September 1999, doi:10.1109/34.790437
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