Relaxation optimizing processes in extended probabilistic space

Author

Horiuchi, T. ; Toraichi, K. ; Yamamoto, K. ; Yamada, H.

Author_Institution

Inst. of Inf. Sci. & Electron., Tsukuba Univ., Ibaraki, Japan

Volume

1

fYear

1995

fDate

14-16 Aug 1995

Firstpage

266

Abstract

Classical probabilistic relaxation method has been widely used for solving optimisation problems in various fields, including image processing and pattern recognition. However we realize that there exist cases in which a probability theoretic model is not adequate, especially there exists incompleteness in available information by noises. In order to solve the problem, this paper proposes a relaxation matching method based on Dempster-Shafer theory. Then the update process in probabilistic relaxation method is derived as a special case of Dempster´s combination rule (1967) in DS theory

Keywords

image recognition; iterative methods; probability; Dempster-Shafer theory; extended probabilistic space; image processing; incompleteness; pattern recognition; probability theoretic model; relaxation matching method; relaxation optimizing processes; Character recognition; Dictionaries; Image processing; Ink; Integrated circuit modeling; Integrated circuit noise; Iterative methods; Nose; Pattern recognition; Relaxation methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on

Conference_Location

Montreal, Que.

Print_ISBN

0-8186-7128-9

Type

conf

DOI

10.1109/ICDAR.1995.598991

Filename

598991