SEGMENTING IMAGES USING NORMALIZED COLOR

An algorithm for segmenting images of 3-D scenes is presented. From an input color image, the algorithm determines the number of materials in the scene and labels each pixel according to the corresponding material. This segmentation is useful for many visual tasks including 3-D inspection and 3-D object recognition. The segmentation algorithm is based on a detailed analysis of the physics underlying color image formation and may be applied to images of a wide range of materials and surface textures. An initial edge detection on the intensity image is used to guide the segmentation process and to ensure accurate localization of region boundaries. The algorithm is based on the computation of local image features and can be mapped effectively onto high performance parallel hardware. Issues related to illumination and sensors are addressed. Experimental results obtained for several images are presented.