Many significant and commercially important uses of modem computer technology relate to images. These include image processing, image analysis and computer vision applications. A challenge in the utilization of computers to accurately and correctly perform operations relating to images is the development of algorithms that truly reflect and represent physical phenomena occurring in the visual world. For example, the ability of a computer to correctly and accurately distinguish between a shadow and a material object edge within an image has been a persistent challenge to scientists. Edge detection is a fundamental task in image processing because without accurate and correct detection of the edges of physical objects, no other processing of the image is possible. If a cast shadow is indistinguishable from the object casting the shadow, it would not be possible for the computer to recognize the object.
Typically, commercially available digital cameras record images in a series of pixels. Each pixel comprises digital values corresponding to a set of color bands, for example, most commonly, red, green and blue color components (RGB) of the picture element. While the RGB representation of a scene recorded in an image is acceptable for viewing the image in an aesthetically pleasing color depiction, the red, green and blue bands, with typical commercially acceptable dynamic ranges, may not be optimal for computer processing of the recorded image.
For example, a situation in an image may occur wherein a particular material under a first illumination flux is indistinguishable from a different material under a second, different illumination flux. In such a situation, two pixels of the image, each corresponding to a different material, have nearly identical color values. If a first, lit bluish material depicted in an image has an RGB value of (25, 30, 35) and a second white material in a shadow at the time the image was recorded, also has an RGB value of (25, 30, 35), then the two materials are indistinguishable. If the white material was in a fully lit condition, it would have an RGB value of (250, 250, 250). The presence of indistinguishable color values in an image can confuse results of an image analysis, to, for example, segregate illumination from material color.
When two materials under different illumination conditions are indistinguishable in terms of, for example, RGB color values, calculations concerning the presence of a shadow can result in false positive or false negative findings. Thus, it would be beneficial to provide a method to optimize an image so as to minimize the possibility of phenomena such as indistinguishable color values among different materials, during computer processing of an image.