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ICCV
2009
IEEE

Sparse Representation of Cast Shadows via L1-Regularized Least Squares

14 years 9 months ago
Sparse Representation of Cast Shadows via L1-Regularized Least Squares
Scenes with cast shadows can produce complex sets of images. These images cannot be well approximated by lowdimensional linear subspaces. However, in this paper we show that the set of images produced by a Lambertian scene with cast shadows can be efficiently represented by a sparse set of images generated by directional light sources. We first model an image with cast shadows as composed of a diffusive part (without cast shadows) and a residual part that captures cast shadows. Then, we express the problem in an `1-regularized least squares formulation, with nonnegativity constraints. This sparse representation enjoys an effective and fast solution, thanks to recent advances in compressive sensing. In experiments on both synthetic and real data, our approach performs favorably in comparison to several previously proposed methods.
Xue Mei, Haibin Ling, David W. Jacobs
Added 13 Jul 2009
Updated 10 Jan 2010
Type Conference
Year 2009
Where ICCV
Authors Xue Mei, Haibin Ling, David W. Jacobs
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