{"title":"图像分割的遗传算法","authors":"Giosuè Lo Bosco","doi":"10.1109/ICIAP.2001.957019","DOIUrl":null,"url":null,"abstract":"The paper describes a new algorithm for image segmentation. It is based on a genetic approach that allow us to consider the segmentation problem as a global optimization problem (GOP). For this purpose, a fitness function, based on the similarity between images, has been defined. The similarity is a function of both the intensity and the spatial position of pixels. Preliminary results, obtained using real images, show a good performance of the segmentation algorithm.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":"{\"title\":\"A genetic algorithm for image segmentation\",\"authors\":\"Giosuè Lo Bosco\",\"doi\":\"10.1109/ICIAP.2001.957019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes a new algorithm for image segmentation. It is based on a genetic approach that allow us to consider the segmentation problem as a global optimization problem (GOP). For this purpose, a fitness function, based on the similarity between images, has been defined. The similarity is a function of both the intensity and the spatial position of pixels. Preliminary results, obtained using real images, show a good performance of the segmentation algorithm.\",\"PeriodicalId\":365627,\"journal\":{\"name\":\"Proceedings 11th International Conference on Image Analysis and Processing\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"45\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 11th International Conference on Image Analysis and Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIAP.2001.957019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 11th International Conference on Image Analysis and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIAP.2001.957019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper describes a new algorithm for image segmentation. It is based on a genetic approach that allow us to consider the segmentation problem as a global optimization problem (GOP). For this purpose, a fitness function, based on the similarity between images, has been defined. The similarity is a function of both the intensity and the spatial position of pixels. Preliminary results, obtained using real images, show a good performance of the segmentation algorithm.
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