Total absolute Gaussian curvature for stereo prior

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In spite of the great progress in stereo matching algorithms, the prior models they use, i.e., the assumptions about the probability to see each possible surface, have not changed much in three decades. Here, we introduce a novel prior model motivated by psychophysical experiments. It is based on minimizing the total sum of the absolute value of the Gaussian curvature over the disparity surface. Intuitively, it is similar to rolling and bending a flexible paper to fit to the stereo surface, whereas the conventional prior is more akin to spanning a soap film. Through controlled experiments, we show that the new prior outperforms the conventional models, when compared in the equal setting.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages537-548
Number of pages12
Volume4844 LNCS
EditionPART 2
Publication statusPublished - 2007
Externally publishedYes
Event8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo
Duration: 2007 Nov 182007 Nov 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4844 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th Asian Conference on Computer Vision, ACCV 2007
CityTokyo
Period07/11/1807/11/22

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ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Ishikawa, H. (2007). Total absolute Gaussian curvature for stereo prior. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 4844 LNCS, pp. 537-548). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4844 LNCS, No. PART 2).