Computational Color Constancy with Spatial Correlations

The color of a scene recorded by a trichromatic sensor varies with the spectral distribution of the illuminant. For recognition and many other applications, we seek to process these measurements to obtain a color representation that is unaffected by illumination changes. Achieving such color constancy is an ill-posed problem because both the spectral distribution of the illuminant and the scene reflectance are unknown. For the most part, methods have approached this problem by leveraging the statistics of individual pixel measurements, independent from their spatial contexts. In this work, we show that the strong spatial correlations that exist between measurements at neighboring image points encode useful information about the illuminant and should not be ignored. We develop a method to encode these correlations in a statistical model and exploit them for color constancy. The method is computationally efficient, allows for the incorporation of prior information about natural illuminants, and performs well when evaluated on a large database of

[1]  Bartlett Ms.  The use of transformations. , 1947 .

[2]  M. Bartlett,et al.  The use of transformations. , 1947, Biometrics.

[3]  F. J. Anscombe,et al.  THE TRANSFORMATION OF POISSON, BINOMIAL AND NEGATIVE-BINOMIAL DATA , 1948 .

[4]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[5]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[6]  G. Buchsbaum.  A spatial processor model for object colour perception , 1980 .

[7]  M. H. Brill,et al.  Necessary and sufficient conditions for Von Kries chromatic adaptation to give color constancy , 1982, Journal of mathematical biology.

[8]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[9]  H C Lee,et al.  Method for computing the scene-illuminant chromaticity from specular highlights. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[10]  John K. Tsotsos,et al.  From [R, G, B] to Surface Reflectance: Computing Color Constant Descriptors in Images , 1987, IJCAI.

[11]  Hsien-Hsueh Lee.  Estimating the Illuminant Color from the Shading of a Smooth Surface , 1988 .

[12]  T. Poggio,et al.  Synthesizing a color algorithm from examples. , 1988, Science.

[13]  Mark S. Drew,et al.  Diagonal transforms suffice for color constancy , 1993, 1993 (4th) International Conference on Computer Vision.

[14]  Michael A. Webster,et al.  Human colour perception and its adaptation , 1996 .

[15]  D H Brainard,et al.  Bayesian color constancy. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[17]  Brian V. Funt,et al.  Committee-Based Color Constancy , 1999, CIC.

[18]  Graham D. Finlayson,et al.  Color by Correlation: A Simple, Unifying Framework for Color Constancy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Gerald Schaefer,et al.  Convex and non-convex illuminant constraints for dichromatic colour constancy , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[20]  Levent Sendur,et al.  Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency , 2002, IEEE Trans. Signal Process..

[21]  Aleksandra Pizurica,et al.  A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising , 2002, IEEE Trans. Image Process..

[22]  Kobus Barnard,et al.  Estimating the scene illumination chromaticity by using a neural network. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2002, The Kluwer International Series in Engineering and Computer Science.

[24]  Yücel Altunbasak,et al.  Color plane interpolation using alternating projections , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[25]  Brian V. Funt,et al.  A Large Image Database for Color Constancy Research , 2003, CIC.

[26]  A. Werner.  The spatial tuning of chromatic adaptation , 2003, Vision Research.

[27]  Martin J. Wainwright,et al.  Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..

[28]  W. Freeman,et al.  Exploiting Spatial and Spectral Image Regularities for Color Constancy , 2003 .

[29]  Tom Minka,et al.  Bayesian Color Constancy with Non-Gaussian Models , 2003, NIPS.

[30]  David A. Forsyth,et al.  A novel algorithm for color constancy , 1990, International Journal of Computer Vision.

[31]  Kinjiro Amano,et al.  Information limits on neural identification of colored surfaces in natural scenes , 2004, Visual Neuroscience.

[32]  William R. Mathew,et al.  Color as a Science , 2005 .

[33]  Thomas W. Parks,et al.  Chromatic adaptation and white-balance problem , 2005, IEEE International Conference on Image Processing 2005.

[34]  Keigo Hirakawa,et al.  An Empirical Bayes Em-Wavelet Unification for Simultaneous Denoising, Interpolation, and/Or Demosaicing , 2006, 2006 International Conference on Image Processing.

[35]  K. Gegenfurtner,et al.  Memory modulates color appearance , 2006, Nature Neuroscience.

[36]  Joost van de Weijer,et al.  Author Manuscript, Published in "ieee Transactions on Image Processing Edge-based Color Constancy , 2022 .

[37]  Steven J. Gortler,et al.  The von Kries Hypothesis and a Basis for Color Constancy , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[38]  Andrew Blake,et al.  Bayesian color constancy revisited , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  Karl R Gegenfurtner,et al.  Color appearance of familiar objects: effects of object shape, texture, and illumination changes. , 2008, Journal of vision.

[40]  Keigo Hirakawa,et al.  Color constancy beyond bags of pixels , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Joost van de Weijer,et al.  Generalized Gamut Mapping using Image Derivative Structures for Color Constancy , 2008, International Journal of Computer Vision.

[42]  Ayan Chakrabarti,et al.  An Empirical Camera Model for Internet Color Vision , 2009, BMVC.

[43]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.