Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Optimization-Based Image Segmentation by Genetic Algorithms | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 842029 10 pages doi 2008 842029 Research Article Optimization-Based Image Segmentation by Genetic Algorithms S. Chabrier 1 C. Rosenberger 2 B. Emile 3 and H. Laurent3 1 Laboratoire Terre-OcỀan Universite de la Polynesie Francaise . 6570 98702 Paa a Tahiti Polynesie Pranẹaise France 2Laboratoire GREYC ENSICAEN-Universite de Caen-CNRS 6Boulevard du Marechal Juin 14050 Caen cedex France 3Institut PRISME ENSI de Bourges-Universite d Orleans 88 Boulevard Lahitolle 18020 Bourges cedex France Correspondence should be addressed to H. Laurent Received 24 June 2007 Revised 12 November 2007 Accepted 8 February 2008 Recommended by Ling Guan Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images. Copyright 2008 S. Chabrier et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any .
