Abstract
In process applications, fast and accurate extraction of complex information from an object for the purpose of mechanical processing of that object, is often required. In this paper, a general rule-based approach is developed using a database of measurable geometric "features" and associated complex information. The rules relate the features to the complex processing information. During the on-line processing, the object features are measured and passed into the rule base. The output from the rule base is the complex information that is needed to process the object. A methodology is developed to generate probabilistic rules for the rule base using multivariate probability densities. A knowledge integration scheme is also developed which combines statistical knowledge with expert knowledge in order to improve the reliability and efficiency of information extraction. The rule generation methodology is implemented in a knowledge-based vision system for process information recognition. As an illustrative example, the problem of efficient head removal in an automated salmon processing plant is considered.
Published Version
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