Harris Corner Detection Algorithm Optimization Based on OTSU

Background: Harris corner detection extracting corner features based on the characteristic value of the second order matrix, is regarded as one of the most successful algorithms in corner detection. Methods: OTSU algorithm abbreviation of the maximum variance between clusters can calculate the maximum variance to distinguish the background area and the target area based on the image gray histograms. To obtain the adaptive threshold, Harris needs to artificially select threshold and threshold estimation becomes very difficult. Results: To solve these problems, OHO algorithm is proposed in this paper which aims to optimize the Harris algorithm based on OTSU. The OHO algorithm combines the characteristics of the high accuracy of Harris algorithm and the adaptive threshold selection of OTSU. Conclusion: Experiments show that the OHO algorithm can detect more details and authentic corners, and has better adaptability and robustness than traditional Harris.