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Green Image
Issue No. 08 - Aug. (2012 vol. 34)
ISSN: 0162-8828
pp: 1509-1519
K. Hirakawa , Intell. Signal Syst. Lab., Univ. of Dayton, Dayton, OH, USA
A. Chakrabarti , Sch. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA, USA
T. Zickler , Sch. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA, USA
We introduce an efficient maximum likelihood approach for one part of the color constancy problem: removing from an image the color cast caused by the spectral distribution of the dominating scene illuminant. We do this by developing a statistical model for the spatial distribution of colors in white balanced images (i.e., those that have no color cast), and then using this model to infer illumination parameters as those being most likely under our model. The key observation is that by applying spatial band-pass filters to color images one unveils color distributions that are unimodal, symmetric, and well represented by a simple parametric form. Once these distributions are fit to training data, they enable efficient maximum likelihood estimation of the dominant illuminant in a new image, and they can be combined with statistical prior information about the illuminant in a very natural manner. Experimental evaluation on standard data sets suggests that the approach performs well.
maximum likelihood estimation, band-pass filters, image colour analysis, statistical prior information, spatio-spectral statistics, maximum likelihood approach, color constancy problem, spectral distribution, dominating scene illuminant, white balanced images, spatial band-pass filters, color images, maximum likelihood estimation, Image color analysis, Color, Maximum likelihood estimation, Covariance matrix, Training, Lighting, illumination statistics., Color constancy, statistical modeling, spatial correlations, maximum likelihood

K. Hirakawa, A. Chakrabarti and T. Zickler, "Color Constancy with Spatio-Spectral Statistics," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1509-1519, 2012.
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