Keypoint clustering for robust image matching

Sundeep Vaddadi; Onur Hamsici; Yuriy Reznik; John Hong; Chong Lee

doi:10.1117/12.862359

7 September 2010 Keypoint clustering for robust image matching

Sundeep Vaddadi, Onur Hamsici, Yuriy Reznik, John Hong, Chong Lee

Proceedings Volume 7798, Applications of Digital Image Processing XXXIII; 77980K (2010) https://doi.org/10.1117/12.862359
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

Abstract

A number of popular image matching algorithms such as Scale Invariant Feature Transform (SIFT)1 are based on local image features. They first detect interest points (or keypoints) across an image and then compute descriptors based on patches around them. In this paper, we observe that in textured or feature-rich images, keypoints typically appear in clusters following patterns in the underlying structure. We show that such clustering phenomenon can be used to: 1) enhance recall and precision performance of the descriptor matching process, and 2) improve convergence rate of the RANSAC algorithm used in the geometric verification stage.

Citation Download Citation

Sundeep Vaddadi, Onur Hamsici, Yuriy Reznik, John Hong, and Chong Lee "Keypoint clustering for robust image matching", Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, 77980K (7 September 2010); https://doi.org/10.1117/12.862359

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