Abstract
Expert system technology has been successfully applied to many practical problems, but there has been little evidence of transfer to computer vision. In this paper we discuss some of the problems confronting computer vision, and present an approach to the development of general knowledge-based vision systems. Our approach involves the building of an intermediate symbolic representation of the image data using knowledge-free segmentation processes. From the intermediate level data, a partial interpretation is constructed by associating an object label with selected groups of the intermediate primitives.
The primary mechanism for generation of initial object hypotheses is a rule-based approach applied to the attributes of the lines, regious, and surfacces in the intermediate symbolic representation. Simple rules are defined as ranges over a feature value which are converted to a vote for an object label; complex rules are constructed via a functional combination of the output from the simple rules. The rules are constructed interactively with visual feedback as part of the knowledge engineering process.
The object hypotheses are used to activate portions of the knowledge network related to verifying or more completely extracting the hypotesized object. Once activated, the procedural components of the knowlede network direct additional more expensive extraction of object features, as well as further grouping, splitting and labelling processes at the intermediate level to construct intermediate events which are in closer agreement with the sotred symbolic object description. We conclude with some principles which could be used to guide knowledge-based vision research.
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Riseman, E.M., Hanson, A.R. (1985). A Methodology for the Development of General Knowledge-Based Vision Systems. In: Brauer, W., Radig, B. (eds) Wissensbasierte Systeme. Informatik-Fachberichte, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-70840-4_21
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DOI: https://doi.org/10.1007/978-3-642-70840-4_21
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