On object classification by means of fuzzy sets´ theory

Author

Costin, Hariton

Author_Institution

Res. Inst. for Theor. Inf., Romanian Acad., Romania

fYear

1992

fDate

30 Aug-3 Sep 1992

Firstpage

458

Lastpage

461

Abstract

Presents a practical method for a supervised object classification by means of a decision-making approach using fuzzy sets. The unknown object membership function, as well as the distance between the input symbol and the chosen prototypes, are computed. The classification is made according to the input pattern which maximizes the membership function. The insensitivity of the classification algorithms to the pattern size, misalignment, the possibility of non-complete symbols recognition, and identification of the information source, are accomplished

Keywords

decision theory; feature extraction; fuzzy set theory; image recognition; image segmentation; decision-making approach; feature extraction; fuzzy sets; image recognition; image segmentation; membership function; misalignment; pattern size; supervised object classification; unknown object membership function; Algorithm design and analysis; Brightness; Data mining; Feature extraction; Fuzzy sets; Image edge detection; Image segmentation; Informatics; Pattern recognition; Prototypes;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on

Conference_Location

The Hague

Print_ISBN

0-8186-2915-0

Type

conf

DOI

10.1109/ICPR.1992.201817

Filename

201817