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Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44483

Gehler,  P
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84384

Rother C, Kiefel,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84651

Zhang,  L
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Gehler, P., Rother C, Kiefel, M., Zhang, L., & Schölkopf, B. (2012). Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance. In Advances in Neural Information Processing Systems 24 (pp. 765-773). Red Hook, NY, USA: Curran.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-B88A-1
Zusammenfassung
We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.