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Reseach Article

Selecting Automatically Vision Operators with their Parameters to Accomplish a Segmentation Task

Published on September 2012 by Issam Qaffou, Mohamed Sadgal, Aziz Elfazziki
Software Engineering, Databases and Expert Systems
Foundation of Computer Science USA
SEDEX - Number 2
September 2012
Authors: Issam Qaffou, Mohamed Sadgal, Aziz Elfazziki
64eb0670-8e7c-4e04-af15-dd116d4e5e6b

Issam Qaffou, Mohamed Sadgal, Aziz Elfazziki . Selecting Automatically Vision Operators with their Parameters to Accomplish a Segmentation Task. Software Engineering, Databases and Expert Systems. SEDEX, 2 (September 2012), 23-28.

@article{
author = { Issam Qaffou, Mohamed Sadgal, Aziz Elfazziki },
title = { Selecting Automatically Vision Operators with their Parameters to Accomplish a Segmentation Task },
journal = { Software Engineering, Databases and Expert Systems },
issue_date = { September 2012 },
volume = { SEDEX },
number = { 2 },
month = { September },
year = { 2012 },
issn = 0975-8887,
pages = { 23-28 },
numpages = 6,
url = { /specialissues/sedex/number2/8363-1014/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Software Engineering, Databases and Expert Systems
%A Issam Qaffou
%A Mohamed Sadgal
%A Aziz Elfazziki
%T Selecting Automatically Vision Operators with their Parameters to Accomplish a Segmentation Task
%J Software Engineering, Databases and Expert Systems
%@ 0975-8887
%V SEDEX
%N 2
%P 23-28
%D 2012
%I International Journal of Computer Applications
Abstract

In a vision system, every task needs that the operators to apply should be « well chosen » and their parameters should be also « well adjusted ». The diversity of operators and the multitude of their parameters constitute a big challenge for users. As it is very difficult to make the « right » choice, lack of a specific rule, many disadvantages appear and affect the computation time and especially the quality of results. In this paper we present a multi-agent architecture to learn the best operators to apply and their best parameters for a class of images. Our architecture consists of three types of agents: User Agent, Operator Agent and Parameter Agent. The User Agent determines the phases of treatment, a library of operators and the possible values of their parameters. The Operator Agent constructs all possible combinations of operators and the Parameter Agent, the core of the architecture, adjusts the parameters of each combination by treating a large number of images. Through the reinforcement learning mechanism, our architecture does not consider only the system opportunities but also the user preferences.

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Index Terms

Computer Science
Information Sciences

Keywords

Computer Vision Reinforcement Learning Multi-agent System Parameter Adjustment Operator Selection Q-learning Segmentation