Mutual Information-based Stereo Matching Combined with SIFT Descriptor in Log-chromaticity Color Space

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 nd 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 constr...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Yong Seok Heo (Seoul National University), Kyoung Mu Lee (Seoul National University), Sang Uk Lee (Seoul National University)
• Keywords: Computer Vision, CVPR 2009, Mutual Information, SIFT, Stereo Matching