A Fuzzy Approach to Stereo Vision Using Pyramidal Images with Different Starting Level

Abstract

We propose a stereo matching algorithm based on multiresolution correlation that varies the depth for the resolution level with which to start stereo calculation for each image pixel (or block of pixels). The initial depth depends on the images local characteristics. We propose to use a neural fuzzy approach to calculate the desirable depth for each pixel of one of the matching images and then use this starting depth to proceed with the multiresolution approach. Variable depth correlation reduces the errors caused by coarse levels. At the same time, the new fuzzy heuristic that we propose for calculating the desired depth keeps most of the blocks at a coarse level, thus having little impact on execution time. Variable depth correlation is expected to have little problems with very plain surfaces and borders, but is rather faster than usual algorithms. In the tests, the multiresolution algorithm proposed here performed faster than plain correlation, with much better results