Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images

Abstract

Stereo matching is an open problem in Computer Vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem of how deep (coarse) should the stereo measures start, trading between error minimization and time consumption, by starting stereo calculation at varying resolution levels, for each pixel, according to fuzzy decisions. Our heuristic enhances the overall execution time since it only employs deeper resolution levels when strictly necessary. It also reduces errors because it measures similarity between windows with enough details. We also compare our algorithm with a very fast multi-resolution approach, and one based on fuzzy logic. Our algorithm performs faster and/or better than all those approaches, becoming, thus, a good candidate for robotic vision applications. We also discuss the system architecture that efficiently implements our solution.

Fuzzy-Logic based Effective Contour Representation of Occluded Objects

“…Based on Gradients, σ Fuzzy + ST-WLS (ours) 640 0.2 1.5 ST-WLS [10] 1 -1000 0.2 2.0 WLS [9] 8000 63 1.5 Fuzzy Logic [2] 255 27 1…”

“…Such sparse alignment of depth information would result in poor object elevation [1]. The depth maps must be computed in such a way as to avoid creating any pixel bleeding across the object's boundaries for dense object reconstruction [2]. Conventional methods for object boundary prediction do not rely on patch centers.…”

Genetic stereo matching algorithm with fuzzy fitness

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