Computer vision algorithms that use color information require color constant images to operate correctly. Color constancy of the images is usually achieved in two steps: first the illuminant is detected and then image is transformed with the chromatic adaptation transform (CAT). Existing CAT methods use a single transformation matrix for all the colors of the input image. The method proposed in this paper requires multiple corresponding color pairs between source and target illuminants given by patches of the Macbeth color checker. It uses Delaunay triangulation to divide the color gamut of the input image into small triangles. Each color of the input image is associated with the triangle containing the color point and transformed with a full linear model associated with the triangle. Full linear model is used because diagonal models are known to be inaccurate if channel color matching functions do not have narrow peaks. Objective evaluation showed that the proposed method outperforms existing CAT methods by more than 21%; that is, it performs statistically significantly better than other existing methods. 1. Introduction One of the pillars of color science is the illuminant of the image being treated. Many real-life applications in the field of computer vision require images that are invariant to the illuminant changes. In [1] authors discuss a problem of the fluorescent lamp spectral distribution change over time in their computer vision system for classifying marble plates. Face detectors [2, 3] and face extractors [4] use predefined skin colors to segment the image. These colors accurately present skin tones only on images with standard illumination. Color- and texture-based image search [5] also requires images to be described with illuminant invariant descriptors. The mechanism of human vision system that takes care of the illuminant invariance is called color constancy [6]. In digital world it is usually modelled as a two-step process consisting of illuminant estimation and image transformation [7]. This paper discusses the second step of the process, that is, image transformation. Image transformations are done with chromatic adaptation transforms (CATs). The conversion from one illuminant to a different one has mostly been handled by using single diagonal von Kries-like transformation [8] for all the colors in the gamut of the input image. The main reason for using a single diagonal model is the fact that usually only one corresponding color pair under two different illuminants is known (i.e., source and target illuminants); thus there is not
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