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 publicationComputer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Pages537-548
Number of pages12
EditionPART 2
ISBN (Print)9783540763895
DOIs
Publication statusPublished - 2007 Jan 1
Externally publishedYes
Event8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, Japan
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)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th Asian Conference on Computer Vision, ACCV 2007
CountryJapan
CityTokyo
Period07/11/1807/11/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Total absolute Gaussian curvature for stereo prior'. Together they form a unique fingerprint.

  • Cite this

    Ishikawa, H. (2007). Total absolute Gaussian curvature for stereo prior. In Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings (PART 2 ed., 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). Springer Verlag. https://doi.org/10.1007/978-3-540-76390-1_53