Fuzzy sets in image processing and recognition

Author

Pal, Sankar K.

Author_Institution

NASA Johnson Space Center, Houston, TX, USA

fYear

1992

fDate

8-12 Mar 1992

Firstpage

119

Lastpage

126

Abstract

The various aspects of image processing and analysis problems where the theory of fuzzy set has so far been applied are addressed along with their relevance and applications. The possibility of combining fuzzy set theory, neural network theory and genetic algorithms for improved performance is discussed. The applications include enhancement, edge detection, thinning, segmentation, object extraction, skeleton extraction, primitive extraction, information and ambiguity measures, curve fitting, and the use of neural learning. Some future research directions are outlined. A list of representative references is also provided

Keywords

fuzzy set theory; image processing; image recognition; neural nets; ambiguity measures; curve fitting; edge detection; fuzzy set theory; genetic algorithms; image enhancement; image processing; image recognition; neural learning; neural network; object extraction; primitive extraction; segmentation; skeleton extraction; thinning; Data mining; Fuzzy set theory; Fuzzy sets; Genetic algorithms; Image analysis; Image edge detection; Image processing; Image recognition; Image segmentation; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1992., IEEE International Conference on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0236-2

Type

conf

DOI

10.1109/FUZZY.1992.258606

Filename

258606