A parallel any-time control algorithm for image understanding

Author

Fischer, V. ; Niemann, H.

Author_Institution

Lehrstuhl fur Mustererkennung, Erlangen-Nurnberg Univ., Germany

Volume

1

fYear

1996

fDate

25-29 Aug 1996

Firstpage

141

Abstract

This paper presents a problem-independent control algorithm for image understanding based on a semantic network formalism for knowledge representation. The algorithm focusses on the achievement of any-time-characteristics, which are important for real world applications. For that purpose, knowledge based image understanding is treated as an optimization problem and solved by iterative combinatorial optimization procedures, like e.g. genetic algorithms. Parallel processing of knowledge and the parallelization of optimization algorithms are shown to provide an efficient approach for an improved any-time-behaviour

Keywords

semantic networks; any-time-characteristics; genetic algorithms; image understanding; iterative combinatorial optimization; knowledge representation; parallel any-time control algorithm; semantic network formalism; Application software; Concrete; Concurrent computing; Genetic algorithms; Image sensors; Image sequences; Iterative algorithms; Knowledge representation; Parallel processing; Sensor phenomena and characterization;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1996., Proceedings of the 13th International Conference on

Conference_Location

Vienna

ISSN

1051-4651

Print_ISBN

0-8186-7282-X

Type

conf

DOI

10.1109/ICPR.1996.546007

Filename

546007