{"title":"轮廓分组的马尔可夫模型","authors":"Sabine Urago, J. Zerubia, M. Berthod","doi":"10.1109/ICPR.1994.576357","DOIUrl":null,"url":null,"abstract":"Describes an algorithm which restores images of incomplete contours using a Markovian model. In order to complete the boundaries, a criterion is defined and introduced in an energy function, which has to be optimized. A deterministic relaxation algorithm ICM (\"iterated conditional mode\") is implemented to minimize this energy function. It generates a configuration in which the contours are reconstructed. Several examples of real image restoration have been tested on a SIMD computer (Connection Machine CM 200). This algorithm allows one to fill up large gaps and to get a better contour grouping.","PeriodicalId":312019,"journal":{"name":"Proceedings of 12th International Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"A Markovian model for contour grouping\",\"authors\":\"Sabine Urago, J. Zerubia, M. Berthod\",\"doi\":\"10.1109/ICPR.1994.576357\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Describes an algorithm which restores images of incomplete contours using a Markovian model. In order to complete the boundaries, a criterion is defined and introduced in an energy function, which has to be optimized. A deterministic relaxation algorithm ICM (\\\"iterated conditional mode\\\") is implemented to minimize this energy function. It generates a configuration in which the contours are reconstructed. Several examples of real image restoration have been tested on a SIMD computer (Connection Machine CM 200). This algorithm allows one to fill up large gaps and to get a better contour grouping.\",\"PeriodicalId\":312019,\"journal\":{\"name\":\"Proceedings of 12th International Conference on Pattern Recognition\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 12th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1994.576357\",\"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 of 12th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1994.576357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Describes an algorithm which restores images of incomplete contours using a Markovian model. In order to complete the boundaries, a criterion is defined and introduced in an energy function, which has to be optimized. A deterministic relaxation algorithm ICM ("iterated conditional mode") is implemented to minimize this energy function. It generates a configuration in which the contours are reconstructed. Several examples of real image restoration have been tested on a SIMD computer (Connection Machine CM 200). This algorithm allows one to fill up large gaps and to get a better contour grouping.
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