Stereo using monocular cues within the tensor voting framework.

Full Abstract

We address the fundamental problem of matching in two static images. The remaining challenges are related to occlusion and lack of texture. Our approach addresses these difficulties within a perceptual organization framework, considering both binocular and monocular cues. Initially, matching candidates for all pixels are generated by a combination of matching techniques. The matching candidates are then embedded in disparity space, where perceptual organization takes place in 3D neighborhoods and, thus, does not suffer from problems associated with scanline or image neighborhoods. The assumption is that correct matches produce salient, coherent surfaces, while wrong ones do not. Matching candidates that are consistent with the surfaces are kept and grouped into smooth layers. Thus, we achieve surface segmentation based on geometric and not photometric properties. Surface overextensions, which are due to occlusion, can be corrected by removing matches whose projections are not consistent in color with their neighbors of the same surface in both images. Finally, the projections of the refined surfaces on both images are used to obtain disparity hypotheses for unmatched pixels. The final disparities are selected after a second tensor voting stage, during which information is propagated from more reliable pixels to less reliable ones. We present results on widely used benchmark stereo pairs.

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Author information

Author/s: Mordohai, Philippos (P); Medioni, Gérard (G);

Affiliation: Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599-3715, USA. mordohai(-atsign-)cs.unc.edu

Journal and publication information

Publication Type: Evaluation Studies; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2006-Jun; vol 28 (issue 6) : pp 968-82

Dates: Created 2006/05/26; Completed 2006/06/20; Revised 2006/11/15;

PMID: 16724590, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Cues
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods

Information Storage and Retrieval - methods
Pattern Recognition, Automated - methods
Photogrammetry - methods
Reproducibility of Results
Sensitivity and Specificity
Vision, Binocular
Vision, Monocular

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