A new stereomatching algorithm based on linear features and the fuzzy integral

Bigand, André; Bouwmans, Thierry; Dubus, J. P.

doi:10.1016/s0167-8655(00)00105-7

Cited by 4 publications

(2 citation statements)

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“…Several works use logic fuzzy clustering algorithms in stereo matching in order to accelerate the correspondence process [ 39 – 46 ]; some of these technique achieve real time processing. The idea is to pre-process images, group features by some fuzzy criteria or guide the search so the best match between features can be determined, or at least guided, using a small set of candidate features.…”

Section: State Of the Artmentioning

confidence: 99%

Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images

Medeiros

Gonçalves²,

Frery

2010

Sensors

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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.

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Section: State Of the Artmentioning

confidence: 99%

Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images

Medeiros

Gonçalves²,

Frery

2010

Sensors

View full text Add to dashboard Cite

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“…3D model reconstruction from 2D images is a challenging topic in computer vision and computer graphics communities. There have been many different approaches such as shape from shading [24][25][26][27][28][29][30][31][32], stereo matching [33][34][35][36][37][38], shape from texture [39,40], shape from motion [41][42][43][44][45], etc.…”

Section: D Shape Reconstruction Techniquesmentioning

confidence: 99%