Stereo using monocular cues within the tensor voting framework

Philippos Mordohai, Gérard Medioni

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations

Abstract

We address the fundamental problem of matching two static images. Significant progress has been made in this area, but the correspondence problem has not been solved. Most of the remaining difficulties are caused by occlusion and lack of texture. We propose an approach that addresses these difficulties within a perceptual organization framework, taking into account both binocular and monocular sources of information. Geometric and color information from the scene is used for grouping, complementing each other's strengths. We begin by generating matching hypotheses for every pixel in such a way that a variety of matching techniques can be integrated, thus allowing us to combine their particular advantages. Correct matches are detected based on the support they receive from their neighboring candidate matches in 3-D, after tensor voting. They are grouped into smooth surfaces, the projections of which on the images serve as the reliable set of matches. The use of segmentation based on geometric cues to infer the color distributions of scene surfaces is arguably the most significant contribution of our research. The inferred reliable set of matches guides the generation of disparity hypotheses for the unmatched pixels. The match for an unmatched pixel is selected among a set of candidates as the one that is a good continuation of the surface, and also compatible with the observed color distribution of the surface in both images. Thus, information is propagated from more to less reliable pixels considering both geometric and color information. We present results on standard stereo pairs.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsTomas Pajdla, Jiri Matas
Pages588-601
Number of pages14
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3024
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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