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Complementary Keypoint Descriptors
Clark F. Olson, Sam A. Hoover, Jordan L. Soltman, and Siqi Zhang In Advances in Visual Computing, Lecture Notes in Computer Science, Vol. 10072 (12th International Symposium on Visual Computing), Part I, pages 341-352, December 2016. Download (5591 K) We examine the use of complementary descriptors for keypoint recognition in digital images. The descriptors combine multiple types of information, including shape, color, and texture. We first review several keypoint descriptors and propose new descriptors that use normalized brightness/color spatial histograms. Individual and combined descriptors are compared on a standard data set that varies blur, viewpoint, zoom, rotation, brightness, and compression. Results indicate that substantially improved results can be achieved without greatly increasing keypoint descriptor length, but that the best results combine information from complementary descriptors. |
