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Color lines: image specific color representation

Published: 27 June 2004 Publication History

Abstract

The problem of deciding whether two pixels in an image have the same real world color is a fundamental problem in computer vision. Many color spaces are used in different applications for discriminating color from intensity to create an informative representation of color. The major drawback of all of these representations is that they assume no color distortion. In practice the colors of real world images are distorted both in the scene itself and in the image capturing process. In this work we introduce Color Lines, an image specific color representation that is robust to color distortion and provides a compact and useful representation of the colors in a scene.

References

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http://www.cs.berkeley.edu/projects/vision/. Berkeley Segmentation Dataset.
[2]
http://www.cie.co.at/cie/index.html. CIE Web site.
[3]
http://www.inforamp.net/~poynton/colorfaq.html. Color FAQ.
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R. Fattal, D. Lischinski, and M. Werman. Gradient domain high dynamic range compression. In Proceedings of ACM SIGGRAPH, pages 249-256, July 2002.
[5]
J. Gomes and A. Mojsilovic. A variational approach to recovering a manifold from sample points. In ECCV (2), pages 3-17, 2002.
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M. D. Grossberg and S. K. Nayar. What is the space of camera responses? In Computer Vision and Pattern Recognition, 2003.
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G. Klinker, S. Shafer, and T. Kanade. A physical approach to color image understanding. International Journal of Computer Vision, 1990.
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Y. Ohta, T. Kanade, and T. Sakai. Color information for region segmentation. Computer Graphics and Image Processing, 13(3):222-241, July 1980.

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  • (2018)Single image dehazing using second-generation wavelet transforms and the mean vector L2-normThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-017-1406-534:5(675-688)Online publication date: 1-May-2018
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Published In

cover image Guide Proceedings
CVPR'04: Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
June 2004
1041 pages

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  • IEEE-CS\DATC: IEEE Computer Society

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IEEE Computer Society

United States

Publication History

Published: 27 June 2004

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  • (2019)Improving Shadow Suppression for Illumination Robust Face RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2018.280317941:3(611-624)Online publication date: 1-Mar-2019
  • (2018)Adaptive propagation matting based on transparency of imageMultimedia Tools and Applications10.1007/s11042-017-5357-777:15(19089-19112)Online publication date: 1-Aug-2018
  • (2018)Single image dehazing using second-generation wavelet transforms and the mean vector L2-normThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-017-1406-534:5(675-688)Online publication date: 1-May-2018
  • (2017)Intrinsic Decompositions for Image EditingComputer Graphics Forum10.5555/3128975.312902736:2(593-609)Online publication date: 1-May-2017
  • (2017)Unmixing-Based Soft Color Segmentation for Image ManipulationACM Transactions on Graphics10.1145/3072959.300217636:4(1)Online publication date: 16-Jul-2017
  • (2017)Unmixing-Based Soft Color Segmentation for Image ManipulationACM Transactions on Graphics10.1145/300217636:2(1-19)Online publication date: 23-Mar-2017
  • (2017)Intrinsic Image Decomposition Using Multi-Scale Measurements and SparsityComputer Graphics Forum10.1111/cgf.1287436:6(251-261)Online publication date: 1-Sep-2017
  • (2017)Intrinsic image estimation using near-$$L_0$$L0 sparse optimizationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-015-1205-933:3(355-369)Online publication date: 1-Mar-2017
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  • (2015)An L1 image transform for edge-preserving smoothing and scene-level intrinsic decompositionACM Transactions on Graphics10.1145/276694634:4(1-12)Online publication date: 27-Jul-2015
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