In machine vision and image database applications color is often used as a simple means of segmenting or identifying a specific object. Although color in and of itself is often insufficient to perform such a task reliably it can be used robustly in conjunction with other features or as a means of identifying likely candidate regions. This works well when all of the images are captured with a single camera and under uniform illumination conditions.
Problems arise when capture conditions change.
Color constancy is not only important for machine vision applications but is also important for digital cameras and scanners. Frequently images are captured under varying lighting conditions. This can lead to images having an reddish tone if captured under tungsten illumination or a greenish tone if captured under fluorescent lighting. Color constancy can be used as a means of removing those color casts resulting in a more visually pleasing image. These methods estimate illumination parameters by making a set of prior assumptions about the overall distribution of color values in a typical image, or correspondingly, typical illuminants and surfaces in the world.