Abstract
Radiometric variations between input images can seriously degrade the performance of stereo matching algorithms. In this situation, mutual information is a very popular and powerful measure which can find any global relationship of intensities between two input images taken from unknown sources. The mutual information-based method, however, is still ambiguous or erroneous as regards local radiometric variations, since it only accounts for global variation between images, and does not contain spatial information properly. In this paper, we present a new method based on mutual information combined with SIFT descriptor to find correspondence for images which undergo local as well as global radiometric variations. We transform the input color images to log-chromaticity color space from which a linear relationship can be established. To incorporate spatial information in mutual information, we utilize the SIFT descriptor which includes near pixel gradient histogram to construct a joint probability in log-chromaticity color space. By combining the mutual information as an appearance measure and the SIFT descriptor as a geometric measure, we devise a robust and accurate stereo system. Experimental results show that our method is superior to the state-of-the art algorithms including conventional mutual information-based methods and window correlation methods under various radiometric changes.