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Perceiving Systems Members Publications

Members

Perceiving Systems
  • Research Group Leader
Robust Machine Learning
  • Postdoctoral Researcher
Empirical Inference
  • Director
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Empirical Inference

Publications

Perceiving Systems Empirical Inference Conference Paper Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance Gehler, P., Rother, C., Kiefel, M., Zhang, L., Schölkopf, B. In Advances in Neural Information Processing Systems 24, 765-773, (Editors: Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.), Curran Associates, Inc., Red Hook, NY, USA, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), 2011
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.
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