DocumentCode
2272634
Title
Learning templates from fuzzy examples in structural pattern recognition
Author
Chan, Kwok-Ping
Author_Institution
Dept. of Comput. Sci., Hong Kong Univ., Hong Kong
fYear
1994
fDate
26-29 Jun 1994
Firstpage
608
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/FUZZY.1994.343662
Filename
343662
Link To Document