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Estimation of Multiple Illuminants from a Single Image of Arbitrary Known Geometry

  • Yang Wang
  • Dimitris Samaras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)

Abstract

We present a new method for the detection and estimation of multiple illuminants, using one image of any object with known geometry and Lambertian reflectance. Our method obviates the need to modify the imaged scene by inserting calibration objects of any particular geometry, relying instead on partial knowledge of the geometry of the scene. Thus, the recovered multiple illuminants can be used both for image-based rendering and for shape reconstruction. We first develop our method for the case of a sphere with known size, illuminated by a set of directional light sources. In general, each point of such a sphere will be illuminated by a subset of these sources. We propose a novel, robust way to segment the surface into regions, with each region illuminated by a different set of sources. The regions are separated by boundaries consisting of critical points (points where one illuminant is perpendicular to the normal). Our region-based recursive least-squares method is impervious to noise and missing data and significantly outperforms a previous boundary-based method using spheres[21]. This robustness to missing data is crucial to extending the method to surfaces of arbitrary smooth geometry, other than spheres. We map the normals of the arbitrary shape onto a sphere, which we can then segment, even when only a subset of the normals is available on the scene. We demonstrate experimentally the accuracy of our method, both in detecting the number of light sources and in estimating their directions, by testing on images of a variety of synthetic and real objects.

Keywords

Critical Boundary Sphere Image Angle Threshold Directional Light Source Consecutive Window 
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 2002

Authors and Affiliations

  • Yang Wang
    • 1
  • Dimitris Samaras
    • 1
  1. 1.Computer Science DepartmentState University of New York at Stony BrookUSA

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