Abstract
The importance of applying different kinds of knowledge in image processing has been recognized for a long time. But a common belief is that reasoning and use of explicit knowledge can only be useful in the domain of high level processing and especially for image interpretation. As a result, usually, for the earlier steps of processing, emphasis is on filtering operations for edge detection and localization. But even the most elegant and powerful of these techniques does not give satisfactory results: the higher the accuracy to achieve in e.g. edge localization, the more unsatisfactory appear results of edge detectors. In order to fill this gap in the whole processing chain of vision applications, our contribution describes a knowledge-based approach for this kind of processing.
We are convinced that early processing can benefit from problem solving techniques and from symbolic reasoning. A primary goal of our work is thus to design a knowledge-based system that manages to improve the edge detection and localization in images of simple quasi-polyhedral manufactured parts. An implementation of such a knowledge-based system for early vision is to be described in this contribution.
Work described in this paper has been partially sponsored through ESPRIT Project P2091 VIMP ”Vision-based on-line inspection of manufactured parts”.
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© 1995 Springer-Verlag Berlin Heidelberg
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Boucher, C., Daul, C., Graebling, P., Hirsch, E. (1995). KBED: A Knowledge-Based Edge Detection System. In: Revell, N., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1995. Lecture Notes in Computer Science, vol 978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0049132
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DOI: https://doi.org/10.1007/BFb0049132
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