Approximate Cross Channel Color Mapping from Sparse Color Correspondences

Hasan Sheikh Faridul, Jurgen Stauder, Jonathan Kervec, Alain Tremeau; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, pp. 860-867

Abstract


We propose a color mapping method that compensates color differences between images having a common semantic content such as multiple views of a scene taken from different viewpoints. A so-called color mapping model is usually estimated from color correspondences selected from those images. In this work, we introduce a color mapping that model color change in two steps: first, nonlinear, channel-wise mapping; second, linear, cross-channel mapping. Additionally, unlike many state of the art methods, we estimate the model from sparse matches and do not require dense geometric correspondences. We show that well known cross-channel color change can be estimated from sparse color correspondence. Quantitative and visual benchmark tests show good performance compared to recent methods in literature.

Related Material


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[bibtex]
@InProceedings{Sheikh_2013_ICCV_Workshops,
author = {Hasan Sheikh Faridul and Jurgen Stauder and Jonathan Kervec and Alain Tremeau},
title = {Approximate Cross Channel Color Mapping from Sparse Color Correspondences},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}
}