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Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz
08thFEBRUARY2015

READING

S. Bell, K. Bala, N. Snavely. Intrinsic Images in the Wild. ACM Transactions of Graphics, 2014.

In contrast to the MIT Intrinsic Images dataset [1], Bell et al. introduce a much larger dataset of more realistic scenes. The ground truth was obtained using crowdsourcing. In addition, they present a new intrinsic image algorithm and include thorough comparison to several recent approaches: Garces et al. [2], Zhao et al. [3] and Shen et al. [4]. The dataset can be downloaded online at opensurfaces.cs.cornell.edu/publications/intrinsic/ and the source code is available on GitHub.

  • [1] R. Grosse, M. K. Johnson, E. H. Adelson, W. T. Freeman. Ground truth dataset and baseline evaluations for intrinsic image algorithms. International Conference on Computer Vision, 2009.
  • [2] E. Garces, A. Munoz, J. Lopez-Moreno, D. Gutierrez. Intrinsic images by clustering. Computer Graphics Forum, 2012.
  • [3] Q. Zhao, P. Tan, Q. Dai, L. Shen, E. Wu, S. Lin. A closed-form solution to retinex with nonlocal texture constraints. Transactions on Pattern Analysis and Machine Intelligence, 2012.
  • [4] L. Shen, P. Tan, S. Lin. Intrinsic image decomposition with non-local texture cues. Conference on Computer Vision and Pattern Recognition, 2008.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below or using the following platforms: