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

Abstract

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