Skip to main navigation Skip to search Skip to main content

A robust algorithm for color correction between two stereo images

  • University of Science and Technology of China

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Most multi-camera vision applications assume a single common color response for all cameras. However, significant luminance and chrominance discrepancies among different camera views often exist due to the dissimilar radiometric characteristics of different cameras and the variation of lighting conditions. These discrepancies may severely affect the algorithms that depend on the color correspondence. To address this problem, this paper proposes a robust color correction algorithm. Instead of handling the image as a whole or employing a color calibration object, we compensate for the color discrepancies region by region. The proposed algorithm can avoid the problem that the global correction techniques possiblely give bad correction results in local areas of an image. Many experiments have been done to prove the effectiveness and the robustness of our algorithm. Though we formulate the algorithm in the context of stereo vision, it can be extended to other applications in a straightforward way.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
PublisherSpringer Verlag
Pages405-416
Number of pages12
EditionPART 2
ISBN (Print)3642123031, 9783642123030
DOIs
StatePublished - 2010
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5995 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Color correction
  • Mean-shift
  • OF-SIFT
  • Stereo images

Fingerprint

Dive into the research topics of 'A robust algorithm for color correction between two stereo images'. Together they form a unique fingerprint.

Cite this