Fuzzy logic-based disparity selection using multiple data costs for stereo correspondence

TL;DR: A new method to combine multiple data costs is presented and a fuzzy-based disparity selection method is proposed, which is proposed to solve the problem of disparity selection inereo matching algorithms.

Abstract: Stereo matching algorithms are capable of generating depth maps from two images of the same scene taken simultaneously from two different viewpoints. Traditionally, a single cost function is used to calculate the disparity between corresponding pixels in the left and right images. In the present research, we have considered a combination of simple data costs. A new method to combine multiple data costs is presented and a fuzzy-based disparity selection method is proposed. Experiments with different combinations of parameters are conducted and compared through the Middlebury and Kitti Stereo Vision Benchmark.