An image segmentation method based on the dichromatic reflection model, is introduced. To adapt to changing illumination conditions the image formation process is modelled by the camera characteristics, the reflectance of the object of interest, and the CIE daylight standard. A priori, loci for the body and surface reflection for the object of interest is modeled according to changes of the illumination by CIE daylight standard. That two loci is approximated by two lines and the plane defined by these is used initially for segmentation. In the case of two objects, the image is segmented by the plane which is rotated about the surface locus to minimize Wilks $lambda@. The method is used for segmenting four images ranging in correlated color temperature from 5200 K to 11500 K. To assess its performance the four images were manually segmented into three classes: vegetation, background, and an uncertain class. The method adapted to the changing light condition with total errors ranging from 3% to 12% and higher error rates being in the images with the largest uncertain group. The method was also compared with Bayes minimax criteria for finding the 'best' rotation from which it deviated by only 0.8% on average.
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