Specular-free residual minimization for photometric stereo with unknown light sources

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

Abstract

We address a photometric stereo problem that has unknown lighting conditions. To estimate the shape, reflection properties, and lighting conditions, we employ a nonlinear minimization that searches for parameters that can synthesize images that best fit the input images. A similar approach has been reported previously, but it suffers from slow convergence due to specular reflection parameters. In this paper, we introduce specular-free residual minimization that avoids the negative effects of specular reflection components by projecting the residual onto the complementary space of the light color. The minimization process simultaneously searches for the optimal light color and other parameters. We demonstrate the effectiveness of the proposed method using several real and synthetic image sets.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages178-189
Number of pages12
Volume7087 LNCS
EditionPART1
DOIs
Publication statusPublished - 2011
Event5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011 - Gwangju, Korea, Republic of
Duration: Nov 20 2011Nov 23 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART1
Volume7087 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011
CountryKorea, Republic of
CityGwangju
Period11/20/1111/23/11

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Migita, T., Sogawa, K., & Shakunaga, T. (2011). Specular-free residual minimization for photometric stereo with unknown light sources. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART1 ed., Vol. 7087 LNCS, pp. 178-189). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7087 LNCS, No. PART1). https://doi.org/10.1007/978-3-642-25367-6_16