Cluster-based color space optimizations

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 1172-1179

  Lau, Cheryl ^a   Heidrich, Wolfgang ^a   Mantiuk, Rafal ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b BANGOR UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER BASED APPROACH; CLUSTER-BASED; COLOR GAMUTS; COLOR SPACE; COLOR TRANSFORMATION; GAMUT MAPPING; GRAY SCALE; IMAGE OPTIMIZATION; INFORMATION LOSS; MULTI-PRIMARY; MULTI-SPECTRAL; SOURCE SPACE; TARGET SPACE;

IMAGE PROCESSING; MAPPING; METADATA; OPTIMIZATION;

COLOR;

EID: 84856682178     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126366     Document Type: Conference Paper

References (34)

1. A. Alsam and I. Farup. Colour gamut mapping as a constrained variational problem. 16th Scandinavian Conf. Image Analysis, 5575:109-118, 2009.
2. D. Arthur and S. Vassilvitskii. k-means++: The advantages of careful seeding. In Proc. 18th ACM-SIAM Symp. Discrete Algorithms, pages 1027-1035, 2007.
3. E. Bennett, J. Mason, and L. McMillan. Multispectral bilateral video fusion. IEEE Trans. Image Process., 16(5):1185-1194, 2007.
4. H. Brettel, F. Viénot, and J. Mollon. Computerized simulation of color appearance for dichromats. J. Opt. Soc. Am. A, 14(10):2647-2655, 1997.
5. W. Carper, T. Lillesand, and R. Kiefer. The use of intensityhue- saturation transformations for merging spot panchromatic and multispectral image data. Photogramm. Eng. Remote Sens., 56:459-467, 1990.
6. P. Chavez and A. Kwarteng. Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis. Photogramm. Eng. Remote Sens., 55:339-348, 1989.
7. CIE. Guidelines for the evaluation of gamut mapping algorithms. Technical Report CIE 156:2004, 2004.
8. Daltonize. http://www.daltonize.org, 2011.
9. A. A. Gooch, S. C. Olsen, J. Tumblin, and B. Gooch. Color2gray: Salience-preserving color removal. ACM Trans. Graph., 24(3):634-639, 2005.
10. A. Goshtasby and S. Nikolov. Image fusion: Advances in the state of the art. Information Fusion, 8(2):114-118, 2007.
11. M. Grundland and N. A. Dodgson. Decolorize: Fast, contrast enhancing, color to grayscale conversion. Pattern Recogn., 40(11):2891-2896, 2007. http://www.Eyemaginary.com/Portfolio/Publications.html.
12. Y. Kim, C. Jang, J. Demouth, and S. Lee. Robust colorto- gray via nonlinear global mapping. ACM Trans. Graph., 28(5):161, 2009.
13. R. Kimmel, D. Shaked, M. Elad, and I. Sobel. Spacedependent color gamut mapping: A variational approach. IEEE Trans. Image Process., 14(6):796-803, 2005.
14. D. Krishnan and R. Fergus. Dark flash photography. ACM Trans. Graph., 28(3):96, 2009.
15. G. Kuhn, M. Oliveira, and L. Fernandes. An efficient naturalness- preserving image-recoloring method for dichromats. IEEE Trans. Vis. Comput. Graphics, 14(6):1747-1754, 2008.
16. G. Kuhn, M. Oliveira, and L. Fernandes. An improved contrast enhancing approach for color-to-grayscale mappings. Vis. Comput., 24:505-514, 2008.
17. E. H. Land and J. J. McCann. Lightness and retinex theory. J. Opt. Soc. Am., 61:1-11, 1971.
18. H. Li, B. Manjunath, and S. Mitra. Multisensor image fusion using the wavelet transform. Graph. Models Image Process., 57(3):235-245, 1995.
19. J. McCann. Lessons learned from mondrians applied to real images and color gamuts. In Proc. IS&T/SID 7th Color Imaging Conf., pages 1-8, 1999.
20. J. Meyer and B. Barth. Color gamut matching for hard copy. In Proc. SID Dig., pages 86-89, 1989.
21. J. Morovic. Color Gamut Mapping. JohnWiley & Sons Ltd., 2008.
22. J. Morovic and Y.Wang. A multi-resolution, full-colour spatial gamut mapping algorithm. In Proc. IS&T/SID 11th Color Imaging Conf., pages 282-287, 2003.
23. S. Nakauchi, S. Hatanaka, and S. Usui. Color gamut mapping based on a perceptual image difference measure. Color Research Application, 24(4):280-291, 1999.
24. L. Neumann, M. Cadik, and A. Nemcsics. An efficient perception-based adaptive color to gray transformation. In Proc. Computational Aesthetics, pages 73-80, 2007.
25. G. Piella. A general framework for multiresolution image fusion: from pixels to regions. Information Fusion, 4(4):259-280, 2003.
26. G. Piella. Image fusion for enhanced visualization: A variational approach. Int. J. Comput. Vision, 83(1):1-11, 2009.
27. K. Rasche, R. Geist, and J. Westall. Detail preserving reproduction of color images for monochromats and dichromats. IEEE Comput. Graph. Appl., 25(3):22-30, 2005.
28. K. Rasche, R. Geist, and J. Westall. Re-coloring images for gamuts of lower dimension. Comput. Graph. Forum, 24(3):423-432, 2005.
29. K. Smith, P.-E. Landes, J. Thollot, and K. Myszkowski. Apparent greyscale: A simple and fast conversion to perceptually accurate images and video. Comput. Graph. Forum, 27(2):193-200, 2008.
30. D. A. Socolinsky and L. B. Wolff. Multispectral image visualization through first-order fusion. IEEE Trans. Image Process., 111(8):923-931, 2002.
31. S. Süsstrunk and C. Fredembach. Enhancing the visible with the invisible: Exploiting near-infrared to advance computational photography and computer vision. SID Int. Symp. Dig., 2010.
32. A. Toet, J. van Ruyven, and J. Valeton. Merging thermal and visual images by contrast pyramids. Opt. Eng., 28(7):789-792, 1989.
33. X. Zhang, T. Sim, and X. Miao. Enhancing photographs with near infra-red images. In IEEE Conf. Comput. Vision Pattern Recogn., pages 1-8, 2008.
34. P. Zolliker and K. Simon. Retaining local image information in gamut mapping algorithms. IEEE Trans. Image Process., 16(3):664-672, 2007.