Computer vision
Computer vision algorithms tend to suffer from varying imaging conditions. To make more robust computer vision algorithms it is important to use a color invariant color space. Color invariant color spaces are desensitized to disturbances in the image. One common problem in computer vision is varying light source (color and intensity) between multiple images and within a single image. To properly perform image segmentation and object detection requires an increased need for images that are stable to variations in imaging conditions. Normalizing RGB color space to rgb color system performs a linear transform. Normalized rgb space eliminates the effect of varying intensities from the light source. Uniform surfaces of color with varying geometric features are affected by the angle and intensity of the light source. Where a uniform red surface with a uniform green object placed on top, should easily be segmented. Due to the shape of the 3D object shades are formed preventing uniform fields of color. Normalizing intensity out removes the shadow. A lambertian reflector under a white illumination is defined by the equation below: