Title :
Learning templates from fuzzy examples in structural pattern recognition
Author_Institution :
Dept. of Comput. Sci., Hong Kong Univ., Hong Kong
Abstract :
A fuzzy-attribute graph (FAG) has been proposed to handle fuzziness in the pattern primitives in structural pattern recognition. FAG has the advantage that one can combine several possible definitions into a single template. However, the template requires human expert definition. In this paper, the author proposes an algorithm that can, from a number of fuzzy instances, find a template that can be matched to the patterns by the original matching metric
Keywords :
attribute grammars; fuzzy set theory; graph theory; learning (artificial intelligence); pattern recognition; fuzzy examples; fuzzy instances; fuzzy-attribute graph; pattern primitives; structural pattern recognition; templates learning; Computer science; Fuzzy set theory; Fuzzy sets; Humans; Layout; Pattern analysis; Pattern matching; Pattern recognition; Uncertainty;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343662
