SIFT image matching algorithm using fusion color information

Yuntian Zheng; Linyu Wang

doi:10.1117/12.2640911

2 December 2022 SIFT image matching algorithm using fusion color information

Yuntian Zheng, Linyu Wang

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 1228813 (2022) https://doi.org/10.1117/12.2640911
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

In order to address the issue that (scale invariant feature transformation,SIFT) algorithm will lose the original image color information, resulting in poor matching ability after graying the color image, this study proposes a SIFT image matching algorithm that incorporates color information . To get the grayscale image to be matched, the algorithm employs a bimodal distribution function to minimize the grayscale difference between pixels and the color contrast between pixels; On this basis, the nearest neighbor distance matching method using Hellinger distance as similarity measure and vector space cosine similarity matching method are combined for feature matching, so as to effectively maintain the color contrast of the original image, improve the number of effective feature points of adjacent images, eliminate the wrong matching point pairs and reduce the wrong matching. The algorithm can effectively discriminate regions with similar gray levels but distinct hues, enhance the matching accuracy, and preserve real-time performance, according to experimental results.

Citation Download Citation

Yuntian Zheng and Linyu Wang "SIFT image matching algorithm using fusion color information", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 1228813 (2 December 2022); https://doi.org/10.1117/12.2640911

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