cv.snu.ac.kr
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 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.
Yong Seok Heo, Kyoung Mu Lee, Sang Uk Lee
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