Abstract
We propose an approach to object recognition based on low level image pattern features. This approach is intended to avoid creating complex high level object model. Our goal is to achieve “quick and (possibly) rough” object recognition from images. An object is defined by a collection of patterns and relations among them. A pattern is a visual clue to an object, such as color, edges texture, etc. Operations like edge detection and region segmentation are performed in a predefined area, which is a small window over which the pattern is defined. Relations among patterns are used to provide constraints on the search space for patterns and for an intelligent pattern matching. Fuzzy variables are used to express patterns in order to build the system's tolerance to imprecision. Sample data is summarized into fuzzy sets. Pattern matching is realized by fuzzy reasoning. The overall evaluation of the matching can be provided by aggregations (i.e. fuzzy integral).
On leave from the Computer Science Department, University of Cincinnati, Cincinnati Ohio, U. S. A.
This work was partially supported by the NSF Grant INT91-08632.
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© 1995 Springer-Verlag Berlin Heidelberg
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Zhang, W., Ralescu, A. (1995). Object recognition based on pattern features. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Advances in Intelligent Computing — IPMU '94. IPMU 1994. Lecture Notes in Computer Science, vol 945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035989
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DOI: https://doi.org/10.1007/BFb0035989
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