4 March 2015 Building reliable keypoint matches by a cascade of classifiers with resurrection mechanism
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Abstract
This paper presents a cascade of classifiers with “resurrection” mechanism for building reliable keypoint matches. It is likely to cause that correct keypoint mappings are removed because of too strict regulation in many existing solutions of image registration. To avoid this situation and get accuracy result, a cascade framework with multi-steps is proposed to remove the incorrect keypoint mappings. To further reduce the rate of misjudgment to correct mappings in each step, we introduce “resurrection” in a cascade structure. Keypoint mappings are initially built with their associated descriptors, and then in each step part of keypoint mappings are determined to be incorrect and deleted completely. Meanwhile, some mappings which perform relatively poor are undetermined and their fate will be decided in next step under their performance. By this means, we use multi-steps efficiently and reduce misjudgment to correct mappings. Experimental results show that the presented cascade structure can robustly remove the outlier keypoint mappings and achieve accurate image registration.
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Jing Jing, Yong Li, Chunxiao Fan, Wei Qiao, Hongbin Jin, "Building reliable keypoint matches by a cascade of classifiers with resurrection mechanism", Proc. SPIE 9410, Visual Information Processing and Communication VI, 94100P (4 March 2015); doi: 10.1117/12.2077339; https://doi.org/10.1117/12.2077339
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