Performance evaluation of SIFT under low light contrast
Hao Wang, Songzhi Jin, Xiaodan Wei, Cong Zhang, Ruiguang Hu · 2020
As an excellent method for extracting distinctive invariant features from images, SIFT (scale-invariant feature transform) can effectively resist affine transformation such as translation and rotation of images, and theoretically has better resistance to illumination changes [1]. However, in practical applications the performance of SIFT is always affected by the contrast reduction caused by illumination changes. In this paper, the performance of SIFT under different contrasts is systematically analyzed and evaluated, and a reasonable explanation is given for the reason of SIFT performance change under different illumination conditions. And a SIFT fast matching method based on contrast compression is proposed.