Adaptive threshold FAST corner detection algorithm combined with edge information

Zeqian Xu; Junyang Zhao; Zhilli Zhang; Xingxu Yan

doi:10.1117/12.2634476

12 May 2022 Adaptive threshold FAST corner detection algorithm combined with edge information

Zeqian Xu, Junyang Zhao, Zhilli Zhang, Xingxu Yan

Proceedings Volume 12173, International Conference on Optics and Machine Vision (ICOMV 2022); 121731N (2022) https://doi.org/10.1117/12.2634476
Event: International Conference on Optics and Machine Vision (ICOMV 2022), 2022, Guangzhou, China

Abstract

Corner detection is a method used to obtain image features in computer vision systems, and is widely used in motion detection, image matching, video tracking, three-dimensional modeling, and target recognition. Also called feature point detection. Aiming at the shortcomings of traditional fast corner point extraction algorithms that use fixed thresholds when extracting image feature points and are easily affected by illumination to extract external points on a smooth plane, this paper proposes an adaptive threshold FAST corner detection method combined with edge information. First, use the canny algorithm to extract the edge information of the image, and then explain the calculation method of the adaptive threshold, calculate the feature point extraction threshold according to the gray information of the image, and finally extract the FAST corner points from the edge information according to the adaptive threshold. The test results show that: compared with the original FAST corner detection algorithm, the improved algorithm has increased its running speed by more than 30%, and has significantly reduced the number of external points, and its noise resistance has also been significantly improved.

Citation Download Citation

Zeqian Xu, Junyang Zhao, Zhilli Zhang, and Xingxu Yan "Adaptive threshold FAST corner detection algorithm combined with edge information", Proc. SPIE 12173, International Conference on Optics and Machine Vision (ICOMV 2022), 121731N (12 May 2022); https://doi.org/10.1117/12.2634476

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