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Specular-Free Residual Minimization for Photometric Stereo with Unknown Light Sources

  • Tsuyoshi Migita
  • Kazuhiro Sogawa
  • Takeshi Shakunaga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)

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.

Keywords

Input Image Photometric Stereo Point Light Source Preconditioned Conjugate Gradient Method Specular Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tsuyoshi Migita
    • 1
  • Kazuhiro Sogawa
    • 1
  • Takeshi Shakunaga
    • 1
  1. 1.Okayama UniversityJapan

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