Colour, as defined by Dictionary.com, is ‘the quality of an object or substance with respect to light reflected by the object, usually determined visually by measurement of hue, saturation, and brightness of the reflected light; saturation or chroma; hue’. It is visual perceptual property corresponding in humans to the categories called red, blue, yellow and etcetera. Colour is derived from the spectrum of light interacting in the eye with the spectral sensitivities of the light receptors.
It has been said that different people see different colours, such as the case that almost broke the Internet which was the debate about the colour of this dress.
This is because each person has varying spectral sensitivity of different types of cone cells in the retina to different parts of the colour spectrum.
Colour Constancy is the ability to perceive colours of objects, invariant to the colour of the light source. Computational Colour Constancy can follow different paths to maintain stable colour appearance across light sources. One common path, which is now believed not to mimic the human visual system, but is very common among computational models, approaches the problem using two phases. First, based on several assumptions, the colour of the light source is estimated from an input image. Then, using this estimated illuminant, the input image is corrected so that it appears to be taken under a canonical (e.g. white) light source. This approach is outlined in the figure to the left. The original image is recorded under a blueish light source. Assuming uniform illumination across the image, the estimated light source (the blue pane) is used to correct every pixel in the input image. The output image is the ultimate goal, but the main focus is on the first phase: illuminant estimation.
Colour Constancy is the ability to perceive colours of objects, invariant to the colour of the light source. Computational Colour Constancy can follow different paths to maintain stable colour appearance across light sources. One common path, which is now believed not to mimic the human visual system, but is very common among computational models, approaches the problem using two phases. First, based on several assumptions, the colour of the light source is estimated from an input image. Then, using this estimated illuminant, the input image is corrected so that it appears to be taken under a canonical (e.g. white) light source. This approach is outlined in the figure to the left. The original image is recorded under a blueish light source. Assuming uniform illumination across the image, the estimated light source (the blue pane) is used to correct every pixel in the input image. The output image is the ultimate goal, but the main focus is on the first phase: illuminant estimation.
Once the chromaticity of the light source is known, the input image can be corrected. The transformation to convert an input image, recorded under an unknown light source, to an output image that appears to be recorded under a canonical light source, is called chromatic adaptation. Chromatic adaption is often modelled using a linear transformation, which in turn can be simplified by a diagonal transformation when certain conditions are met.
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