Stereo correspondence estimation using multiwavelets scale-space representation-based multiresolution analysis

Bhatti, A. and Nahavandi, S. 2008, Stereo correspondence estimation using multiwavelets scale-space representation-based multiresolution analysis, Cybernetics and systems, vol. 39, no. 6, pp. 641-665, doi: 10.1080/01969720802188193.

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Title Stereo correspondence estimation using multiwavelets scale-space representation-based multiresolution analysis
Author(s) Bhatti, A.ORCID iD for Bhatti, A.
Nahavandi, S.ORCID iD for Nahavandi, S.
Journal name Cybernetics and systems
Volume number 39
Issue number 6
Start page 641
End page 665
Total pages 25
Publisher Taylor & Francis
Place of publication Abingdon, England
Publication date 2008-08
ISSN 0196-9722
Keyword(s) cybernetics
human computer intelligence
Information & Communication Technology (ICT)
machine learning -- design
Summary A multiresolution technique based on multiwavelets scale-space representation for stereo correspondence estimation is presented. The technique uses the well-known coarse-to-fine strategy, involving the calculation of stereo correspondences at the coarsest resolution level with consequent refinement up to the finest level. Vector coefficients of the multiwavelets transform modulus are used as corresponding features, where modulus maxima defines the shift invariant high-level features (multiscale edges) with phase pointing to the normal of the feature surface. The technique addresses the estimation of optimal corresponding points and the corresponding 2D disparity maps. Illuminative variation that can exist between the perspective views of the same scene is controlled using scale normalization at each decomposition level by dividing the details space coefficients with approximation space. The problems of ambiguity, explicitly, and occlusion, implicitly, are addressed by using a geometric topological refinement procedure. Geometric refinement is based on a symbolic tagging procedure introduced to keep only the most consistent matches in consideration. Symbolic tagging is performed based on probability of occurrence and multiple thresholds. The whole procedure is constrained by the uniqueness and continuity of the corresponding stereo features. The comparative performance of the proposed algorithm with eight famous existing algorithms, presented in the literature, is shown to validate the claims of promising performance of the proposed algorithm.
Language eng
DOI 10.1080/01969720802188193
Field of Research 080104 Computer Vision
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2008, Taylor & Francis
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