An intermediate level industrial vision system
There is a trend in manufacturing towards fully automated production facilities in which all operations are integrated by computer based information systems. The current generation of industrial inspection systems lack the necessary flexibility to operate in these environments. AI based Image Understanding systems have the necessary level of generality, achieved through the use of domain specific object models. These models are used to guide early visual processing, and must be supplied to the system. Current theories in cognitive psychology call for a reevaluation of the role of such 'auxiliary' knowledge in early visual processing. Recent work suggests that very general cognitive processes may build up a hierarchical representation of the world. The emphasis is currently on such generic cognitive processes rather than on the use of world knowledge. A novel approach to image processing, in which emphasis is placed on generic low and intermediate level techniques, is proposed in this thesis. This approach, termed the descriptor approach, delays the use of domain specific models until a full description of the image has been produced. A prototype industrial inspection system has been implemented, based on the descriptor approach: the Hierarchical Scene Description (HISD) system. General image features are extracted from images of populated PCBs, and subsequently transformed into a database of prolog facts by an interface subsystem. Finally the intermediate level vision subsystem uses rules to reason about these features, building up a semantic net based description of the scene. HISD successfully builds up hierarchical descriptions of real industrial PCB images in terms of geometric shapes, their coordinates, and spatial relationships between shapes. The results are displayed graphically and are achieved without the use of any object models, thus avoiding the problems of inflexibility and lack of generality associated with more complex model based systems.