Color constancy using natural image statistics

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2007, Pages

  Gijsenij, Arjan ^a   Gevers, Theo ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; LIGHT SOURCES; PARAMETERIZATION; STATISTICS; WEIBULL DISTRIBUTION;

COLOR CONSTANCY; IMAGE CHARACTERISTICS; WEIBULL PARAMETERIZATION;

COLOR IMAGE PROCESSING;

EID: 34948856428     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383206     Document Type: Conference Paper

References (22)

1. D. Brainard and W. Freeman. Bayesian color constancy. J. Optical Society of America A, 14:1393-1411, 1997.
2. G. Buchsbaum. A spatial processor model for object colour perception. J. of the Franklin Institute, 310(1): 1-26, 1980.
3. V. Cardei and B. Funt. Committee-based color constancy. In Proc. of the Seventh Color Imaging Conference, pages 311-313. IS&T - The Society for Imaging Science and Technology, 1999.
4. R Ciurea and B. Funt. A large image database for color constancy research. In Proc. of the Eleventh Color Imaging Conference, pages 160-164. IS&T - The Society for Imaging Science and Technology, 2003.
5. M. D'Zmura, G. Iverson, and B. Singer. Probabilistic color constancy. In Geometric Representations of Perceptual Phenomena, pages 187-202. Lawrence Erlbaum Associates, 1995.
6. M. Ebner. Evolving color constancy. Pattern recognition Letters, 27(11):1220-1229, 2006.
7. G. Finlayson, S. Hordley, and P. Hubel. Color by correlation: a simple, unifying framework for color constancy. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(11):1209-1221, 2001.
8. G. Finlayson, S. Hordley, and I. Tastl. Gamut constrained illuminant estimation. In Int. Conf. on Computer Vision, volume 2, pages 792-799, 2003.
9. G. Finlayson and E. Trezzi. Shades of gray and colour constancy. In Proc. of the Twelfth Color Imaging Conference, pages 37-41. IS&T - The Society for Imaging Science and Technology, 2004.
10. D. Forsyth. A novel algorithm for color constancy. Int. Journal of Compututer Vision, 5(1):5-36, 1990.
11. J. Geusebroek and A. Smeulders. A six-stimulus theory for stochastic texture. Int. Journal of Computer Vision, 62(1-2):7-16, 2005.
12. T. Gevers and A. Smeulders. Pictoseek: combining color and shape invariant features for image retrieval. IEEE Trans. Im. Proc. 9(1):102-119, 2000.
13. S. Hordley. Scene illuminant estimation: past, present, and future. Color Research and Application, 31(4):303-314, 2006.
14. S. Hordley and G. Finlayson. Re-evaluating colour constancy algorithms. In Proc. of the Int. Conf. on Pattern Recognition, volume 1, pages 76-79, Washington, DC, USA, 2004. IEEE Computer Society.
15. L. Kuncheva and J. Bezdek. Presupervised and postsupervised prototype classifier design. IEEE Trans, on Neural Networks, 10(5): 1142-1152, September 1999.
16. E. Land. The retinex theory of color vision. Scientific American, 237(6): 108-128, December 1977.
17. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. Journal of Computer Vision, 42(3): 145-175, 2001.
18. G. Schaefer, S. Hordley, and G. Finlayson. A combined physical and statistical approach to colour constancy. In Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pages 148-153, Washington, DC, USA, 2005. IEEE Computer Society.
19. A. Torralba. Contextual priming for object detection. Int. Journal of Computer Vision, 53(2):169-191, 2003.
20. A. Torralba and A. Oliva. Statistics of natural image statistics. Network: computation in neural systems, 14(3):391-412, 2003.
21. J. van de Weijer and T. Gevers. Color constancy based on the grey-edge hypothesis. In Proc. of IEEE Int. Conf. on Image Processing, October 2005.
22. J. van Gemert, J. Geusebroek, C. Veenman, C. Snoek, and A. Smeulders. Robust scene categorization by learning image statistics in context. In CVPR Workshop on Semantic Learning Applications in Multimedia (SLAM), New York, USA, June 2006.