An Improved Harris Corner Points Detection for Low-Light Scenes Based on Contrast Limited Adaptive Histogram Equalization
Jiawei Guo, Jieming Ma, Ángel F. García‐Fernández, Ji Ge, Yungang Zhang · 2022
Corner points are commonly defined as the intersection of two edges, and the Harris algorithm, which performs corner point detection based on the grey value variation between a patch and its neighborhood, is commonly used in various computer vision tasks. For low-light images, Harris algorithm is affected because the details of the image become blurred by low contrast. This paper proposes an improved Harris algorithm, which is inspired by the Contrast Limited Adaptive Histogram Equalization (CLAHE). After extracting a patch of the target image, the gray value of the patch is adjusted based on the cumulative distribution function (CDF). As a result, the gray value of the patch becomes evenly distributed, and the variation of the gray value of the patch becomes sharper. The improved Harris algorithm has been compared with the original Harris algorithm on different images of low-light scenes. Experimental results show that the proposed algorithm can effectively detect corner points in low contrast regions, and the repeatability of corner points matching in the low-light regions is significantly improved.