Abstract
In this document, we present an alternative to the method introduced by Ebner (Pattern Recognit 60–67, 2003; J Parallel Distrib Comput 64(1):79–88, 2004; Color constancy using local color shifts, pp 276–287, 2004; Color Constancy, 2007; Mach Vis Appl 20(5):283–301, 2009) for computing the local space average color. We show that when the problem is framed as a linear system and the resulting series is solved, there is a solution based on LU decomposition that reduces the computing time by at least an order of magnitude.
Similar content being viewed by others
References
Barnard, K., Cardei, V., Funt, B.: A comparison of computational color constancy algorithms. I: methodology and experiments with synthesized data. IEEE Trans. Image Process. 11(9), 972–984 (2002)
Barnard, K., Finlayson, G., Funt, B.: Colour constancy for scenes with varying illumination. In: ECCV, pp. 1–15 (1996)
Barnard, K., Martin, L., Coath, A., Funt, B.: A comparison of computational color constancy algorithms. II. Experiments with image data. IEEE Trans. Image Process. 11(9), 985–996 (2002)
Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Automatic color constancy algorithm selection and combination. Pattern Recognit. 43(3), 695–705 (2010)
Buchsbaum, G.: A spatial processor model for object colour perception. J. Frankl. Inst. 310(1), 1–26 (1980)
Ebner, M.: Combining white-patch Retinex and the gray world assumption to achieve color constancy for multiple illuminants. In: Pattern Recognition. Lecture Notes in Computer Science, vol 2781, pp 60–67 (2003)
Ebner, M.: A parallel algorithm for color constancy. J. Parallel Distrib. Comput. 64(1), 79–88 (2004)
Ebner, M.: Color constancy using local color shifts. In: ECCV, pp. 276–287 (2004)
Ebner, M.: Color Constancy. Wiley, West Sussex (2007)
Ebner, M.: Color constancy based on local space average color. Mach. Vis. Appl. 20(5), 283–301 (2009)
Finlayson, G., Funt, B., Barnard, K.: Color constancy under varying illumination. In: ICCV, p. 720 (1995)
Finlayson, G., Schiele, B., Crowley, J.: Comprehensive colour image normalization. In: ECCV, pp. 475–490 (1998)
Gijsenij, A., Gevers, T., van de Weijer, J.: Computational color constancy: survey and experiments. IEEE Trans. Image Process. 20(9), 2475–2489 (2011)
Golub, G., van Loan, C.: Matrix Computations. Johns Hopkins University Press, Baltimore (1996)
Livingstone, M.: Vision and Art: the Biology of Seeing. Abrams, New York (2008)
McCann, J.: HDR imaging and color constancy: two sides of the same coin? In: SPIE, vol. 7866, p. 22 (2011)
Palma-Amestoy, R., Provenzi, E., Bertalmío, M., Caselles, V.: A perceptually inspired variational framework for color enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 458–474 (2009)
Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes, vol. 3. Cambridge University Press, New York (2007)
Provenzi, E., Gatta, C., Fierro, M., Rizzi, A.: A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2007)
Wandell, B.: Foundations of Vision. Sinauer Associates, Sunderland (1995)
West, D.: Introduction to Graph Theory, vol. 1. Prentice Hall (2001)
Young Jr., D.: Iterative Methods for Solving Partial Difference Equations of Elliptic Type. Tech. rep. Harvard University (1950)
Acknowledgments
Thanks to Marc Ebner for providing the original images for Fig. 2, to Mary Masterman for editing the document, and to the reviewers whose comments improved greatly the quality of our exposition. This work was partially supported by the Fomix CONACYT-DF under Grant No. 189005 and the Instituto Politecnico Nacional under Grant No. 20131832 for Joaquin Salas, and the National Science Foundation under Grant No. IIS-1017017 and by the Army Research Office under Grant No. W911NF-10-1-0387 for Carlo Tomasi.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Salas, J., Tomasi, C. A linear system form solution to compute the local space average color. Machine Vision and Applications 24, 1555–1560 (2013). https://doi.org/10.1007/s00138-013-0494-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-013-0494-0