A uniform framework for estimating illumination chromaticity, correspondence, and specular reflection

Qingxiong Yang, Shengnan Wang, Narendra Ahuja, Ruigang Yang

Research output: Contribution to journalArticle

Abstract

Based upon a new correspondence matching invariant called illumination chromaticity constancy, we present a new solution for illumination chromaticity estimation, correspondence searching, and specularity removal. Using as few as two images, the core of our method is the computation of a vote distribution for a number of illumination chromaticity hypotheses via correspondence matching. The hypothesis with the highest vote is accepted as correct. The estimated illumination chromaticity is then used together with the new matching invariant to match highlights, which inherently provides solutions for correspondence searching and specularity removal. Our method differs from the previous approaches: those treat these vision problems separately and generally require that specular highlights be detected in a preprocessing step. Also, our method uses more images than previous illumination chromaticity estimation methods, which increases its robustness because more inputs/constraints are used. Experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Article number5497278
Pages (from-to)53-63
Number of pages11
JournalIEEE Transactions on Image Processing
Volume20
Issue number1
DOIs
StatePublished - Jan 1 2011

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Keywords

  • Chromaticity
  • dichromatic reflection model
  • reflection components separation
  • specular reflection
  • stereo matching

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Cite this

A uniform framework for estimating illumination chromaticity, correspondence, and specular reflection. / Yang, Qingxiong; Wang, Shengnan; Ahuja, Narendra; Yang, Ruigang.

In: IEEE Transactions on Image Processing, Vol. 20, No. 1, 5497278, 01.01.2011, p. 53-63.

Research output: Contribution to journalArticle

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