Title :
On object classification by means of fuzzy sets´ theory
Author_Institution :
Res. Inst. for Theor. Inf., Romanian Acad., Romania
fDate :
30 Aug-3 Sep 1992
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;
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
DOI :
10.1109/ICPR.1992.201817
