Fuzzy Logic Based Stereo Matching Method for Images with Variation in Exposure Conditions

Disparity maps generated through stereo matching algorithms possess the capacity to provide depth information, when at least two or more images of a scene taken from different viewpoints, are presented. This is a computationally complex task and the presence of radiometric differences, such as exposure variations, in the images only further complicates the stereo matching problem. The authors attempt to overcome this problem and try to extract dense disparity maps from a pair of stereo images using a combination of different data cost metrics followed by a fuzzy disparity selector. The images are preprocessed into small patches of pixels, such that pixels in each patch have similar intensities, before being subjected to the stereo matching algorithm. The effect of the number of segments and the tuning parameter ‘α’, on the various exposure conditions is studied and the performance is compared with other methods that try to tackle the problem of stereo matching under similar conditions.