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Adaptive colour constancy algorithm using discrete wavelet transform

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Çelik, Turgay and Tjahjadi, Tardi. (2012) Adaptive colour constancy algorithm using discrete wavelet transform. Computer Vision and Image Understanding, Vol.116 (No.4). pp. 561-571. ISSN 1077-3142

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Official URL: http://dx.doi.org/10.1016/j.cviu.2011.12.004

Abstract

The colours of chromatically homogeneous object surfaces measured by a sensor vary with the illuminant colour used to illuminate the objects. In contrast, colour constancy enables humans to identify the true colours of the surfaces under varying illumination. This paper proposes an adaptive colour constancy algorithm which estimates the illuminant colour from wavelet coefficients at each scale of the decomposition by discrete wavelet transform of the input image. The angular error between the estimated illuminant colours in consecutive scales are used to determine the optimum scale for the best estimate of the true illuminant colour. The estimated illuminant colour is then used to modify the approximation subbands of the image so as to generate the illuminant-colour corrected image via inverse discrete wavelet transform. The experiments show that the colour constancy results generated by the proposed algorithm are comparable or better than those of the state-of-the-art colour constancy algorithms that use low-level image features.

Item Type:	Journal Article
Subjects:	Q Science > QC Physics T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Image processing -- Digital techniques, Wavelets (Mathematics)
Journal or Publication Title:	Computer Vision and Image Understanding
Publisher:	Academic Press
ISSN:	1077-3142
Official Date:	April 2012
Volume:	Vol.116
Number:	No.4
Number of Pages:	11
Page Range:	pp. 561-571
Identification Number:	10.1016/j.cviu.2011.12.004
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
References:	[1] M. Ebner, Color Constancy, 1st Edition, Wiley Publishing, 2007. [2] K. Barnard, V. Cardei, B. Funt, A comparison of computational color constancy algorithms. i: Methodology and experi- ments with synthesized data, IEEE Transactions on Image Processing 11 (9) (2002) 972–984. [3] J.-M. Geusebroek, R. van den Boomgaard, A. Smeulders, H. Geerts, Color invariance, IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (12) (2001) 1338–1350. [4] G. Finlayson, S. Hordley, P. HubeL, Color by correlation: a simple, unifying framework for color constancy, IEEE Trans- actions on Pattern Analysis and Machine Intelligence 23 (11) (2001) 1209–1221. [5] D. H. Brainard, W. T. Freeman, Bayesian color constancy, The Journal of the Optical Society of America A 14 (7) (1997) 1393–1411. [6] V. C. Cardei, B. Funt, K. Barnard, Estimating the scene illumination chromaticity by using a neural network, The Journal of the Optical Society of America A 19 (12) (2002) 2374–2386. [7] P. V. Gehler, C. Rother, A. Blake, T. Minka, T. Sharp, Bayesian color constancy revisited, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8. [8] D. A. Forsyth, A novel algorithm for color constancy, International Journal of Computer Vision 5 (1) (1990) 5–36. [9] G. D. Finlayson, S. D. Hordley, Gamut constrained illuminant estimation, International Journal of Computer Vision 67 (1) (2006) 93–109. [10] J. van de Weijer, T. Gevers, A. Gijsenij, Edge-based color constancy, IEEE Transactions on Image Processing 16 (9) (2007) 2207–2214. [11] K. Barnard, L. Martin, A. Coath, B. Funt, A comparison of computational color constancy algorithms. ii. experiments with image data, IEEE Transactions on Image Processing 11 (9) (2002) 985–996. [12] E. H. Land, J. J. McCANN, Lightness and retinex theory, The Journal of the Optical Society of America A 61 (1) (1971) 1–11. [13] G. Buchsbaum, A spatial processor model for object colour perception, Journal of the Franklin Institute 310 (1980) 1–26. [14] G. Finlayson, E. Trezzi, Shades of gray and colour constancy, in: Proceedings of IS&T/SID Twelfth Color Imaging Conference, 2004, pp. 37–41. [15] M. Ebner, G. Tischler, J. Albert, Integrating color constancy into jpeg2000, IEEE Transactions on Image Processing 16 (11) (2007) 2697–2706. [16] I. Daubechies, Ten lectures on wavelets, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1992. [17] S. Mallat, A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way, Academic Press, 2008. [18] S. D. Hordley, G. D. Finlayson, Reevaluation of color constancy algorithm performance, The Journal of the Optical Society of America A 23 (5) (2006) 1008–1020. [19] K. Barnard, L. Martin, B. Funt, A. Coath, A data set for color research, Color Research and Application 27 (3) (2002) 148–152. [20] F. Ciurea, B. Funt, A large image database for color constancy research, in: Proceedings of IS&T/SID Eleventh Color Imaging Conference, 2003, pp. 160–164.
URI:	http://wrap.warwick.ac.uk/id/eprint/41296