An Adaptive Contrast Threshold SIFT Algorithm Based on Local Extreme Point and Image Texture
Yunwei Jia, Kun Wang, Chenxiang Hao · 2019
SIFT algorithm has good effect for most images in feature matching. But it cannot extract plenty of feature points from blurred images, and the feature points extracted from low-light images have cluster effect seriously. That is because the value of contrast threshold which was used to remove the low responsive-point is fixed. This paper proposes an adaptive contrast threshold SIFT algorithm to improve this situation. The value of contrast threshold is determined by the average value of local extreme points and the texture character of the whole image. Experimental results show that the improved SIFT algorithm can extract plenty of feature points, which are effective, from blurred images and improve the distributed uniformity of the feature points extracted from low-light images.