Abstract
In this paper, a method of pattern recognition based on images split into a set of trees composed of fuzzy regions is presented. First, a fuzzy segmentation based on possibilistic c-means is carried out in the raster image. Fuzzy support have been defined from a first level cut. On each cluster, a fuzzy region is assumed to be a convex combination of sets with associated features. A set of sample trees is achieved from the application of the segmentation algorithm on characteristic objects. Then, a tree isomorphism to recognize is defined to recognize an object. At last, a new tree compression method is introduced to decrease the complexity when we have to manage with a large set of trees.
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CITATION STYLE
Wendling, L., Desachy, J., & Paries, A. (1997). Pattern recognition from compressed labelled trees of fuzzy regions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1310, pp. 174–181). Springer Verlag. https://doi.org/10.1007/3-540-63507-6_199
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