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Low Level Image Segmentation: An Expert System
May 1984 (vol. 6 no. 5)
pp. 555-577
Ahmed M. Nazif, Department of Electrical Engineering, University of Cairo, Cairo, Egypt.
Martin D. Levine, Computer Vision and Robotics Laboratory, Department of Electrical Engineering, McGill University, Montreal, P.Q., Canada.
A major problem in robotic vision is the segmentation of images of natural scenes in order to understand their content. This paper presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include metarules that embody inferences about the order in which the knowledge rules are matched. They also incorporate focus of attention rules that determine the path of processing within the image. Furthermore, an additional set of higher level rules dynamically alters the processing strategy. This paper discusses the structure and content of the knowledge and control rules for image segmentation.
Citation:
Ahmed M. Nazif, Martin D. Levine, "Low Level Image Segmentation: An Expert System," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 5, pp. 555-577, May 1984, doi:10.1109/TPAMI.1984.4767570
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