A multi-agent approach to edge detection as a distributed optimization problem

Author

Spinu, C. ; Garbay, C. ; Chassery, J.M.

Author_Institution

Inst. Albert Bonniot, IMAG, La Tronche, France

Volume

2

fYear

1996

fDate

25-29 Aug 1996

Firstpage

81

Abstract

The purpose of the paper is to describe a multi-agent approach to edge detection as a distributed optimization problem. In this framework, edge detection is seen as a goal to be reached, expressed in terms of the minimization of an estimated edge detection error with respect to an ideal reference which is not explicitly known. Furthermore, an original method for distributed optimization is illustrated based on an initial partitioning of the image into zones corresponding to different characteristics. The initial partitioning can be further refined, based on the evaluation of the result obtained so far. Consistency between adjacent zones in the image is also taken into account. The implementation of this method as a multi-agent system is presented demonstrating the interest in using such systems for solving distributed optimization problems

Keywords

computer vision; edge detection; image segmentation; inference mechanisms; optimisation; software agents; COALA; computer vision; consistency; distributed optimization; edge detection; image segmentation; inference engine; knowledge processors; knowledge servers; multiple agent system; Cost function; Error correction; Image edge detection; Image segmentation; Knowledge based systems; Multiagent systems; Optimization methods;

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.1996.546728

Filename

546728