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http://archives.univ-biskra.dz/handle/123456789/2351Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kamal E. Melkemi | - |
| dc.contributor.author | Sebti Foufou | - |
| dc.date.accessioned | 2014-04-18T11:52:23Z | - |
| dc.date.available | 2014-04-18T11:52:23Z | - |
| dc.date.issued | 2014-04-18 | - |
| dc.identifier.uri | http://archives.univ-biskra.dz/handle/123456789/2351 | - |
| dc.description.abstract | 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. DOI: 10.2498/cit.1001889 Link http://cit.srce.unizg.hr/index.php/CIT/article/view/1889 | en_US |
| dc.language.iso | en | en_US |
| dc.subject | image segmentation, fuzzy logic, Markov, random field, multiagent systems, genetic algorithms, chaotic system. | en_US |
| dc.title | Fuzzy Distributed Genetic Approaches for Image Segmentation | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Publications Internationales | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Fuzzy Distributed Genetic Approaches for Image Segmentation.pdf | 35,7 kB | Adobe PDF | View/Open |
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