Illumination-robust area-based stereo matching with improved census transform
Xin Luan, Fangjie Yu, Honghong Zhou, Xiufang Li, Dalei Song, Bingwei Wu · 2012
This paper presents a novel area-based stereo matching algorithm based on improved census transform under changing illumination. For the traditional census-based stereo matching, the result is not robust under variant illumination, because the intensity value of center pixel in the mask is affected by the noise to cause distortion. In order to solve this problem, we propose the improved census transform method, which takes the standard deviation of census mask as the base point instead of the center pixel, comparing with the difference of per neighborhood pixel and the mean intensity of the mask to build the sparse census transform. The experiments show that the stereo matching algorithm is robust even if the illumination changes.