It is the goal of artificial intelligence and robotics to develop au tom atic system s (robots) th a t are able to ac t in and in teract w ith a com plex en­ vironm ent and dem onstrate a perform ance th a t is a t least com parable to th a t of hum ans. One of the im portan t subtasks in this field is to gather inform ation abou t the environm ent dynam ically through sensing devices and to interpret this inform ation so th a t the robot can (re)act accordingly. In th is area, com puter vision has concentrated on gathering and in terp ret­ ing visual inform ation. Im age understanding involves interpreting two­ dim ensional images which are projections of three-dim ensional scenes. The goal is to ex tract useful inform ation from the images to plan (re)actions. Such inform ation includes a description of the scene as a collection of shiny and m atte surfaces, sm ooth and rough, in teracting w ith light, shape, and shadow. U nfortunately, light interacts in m any com plex ways w ith m a t­ ter, producing optical effects such as shadow casting, object shading and highlights, and due to the inform ation lost during the projection process, com puter vision has not yet been successful a t deriving an appropriate de­ scription of such surface and illum ination properties from an image. The m ain reason has been a lack of models rich enough to relate pixels and pixel-aggregates to scene characteristics. Furtherm ore, m ost im age anal­ ysis concentrates on interpreting only intensity d a ta in black-and-w hite images. Such images do not provide enough information to model optical

effects. T his book presents an approach to com puter vision th a t uses color inform ation to in terpret the effects of shading and highlights in a scene. It exploits a physical reflection m odel which relates shading and highlights to color variation in the im age.