<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Kamal E. Melkemi, Sebti Foufou: Fuzzy Distributed Genetic Approaches for Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">CIT Journal of Computing and Information Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">cit.fer.hr/index.php/CIT/article/view/1889</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%"> 221–231</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a new image segmentation algorithm (called FDGA-Seg) based on a combination of fuzzy logic, multiagent systems and genetic algorithms. We propose to use a fuzzy representation of the image site labels by introducing some imprecision in the gray tones values. The distributivity of FDGA-Seg comes from the fact that it is designed around a MultiAgent System (MAS) working with two different architectures based on the master-slave and island models. A rich set of experimental segmentation results given by FDGA-Seg is discussed and compared to the ICM results in the last section.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>