Ahmed Kirmani, Naveen Goela, N. Chatterjee, Ben Vigoda
{"title":"活动轮廓的消息传递算法","authors":"Ahmed Kirmani, Naveen Goela, N. Chatterjee, Ben Vigoda","doi":"10.1109/ICASSP.2008.4518053","DOIUrl":null,"url":null,"abstract":"Many important early vision techniques, such as active contours (ACs), can be formulated as energy minimization. However, finding global optimum (minimum energy) configurations is often computationally intractable. Approximate solutions obtained using iterative numerical methods may be ill-conditioned, and exhibit poor convergence and inaccuracy due to noise and discretization errors. We formulate AC as a statistical estimation problem and solve it using (Gaussian) message passing on factor graphs of linear models. The resulting algorithm exhibits faster convergence and the solutions possess higher numerical stability, robustness and accuracy.","PeriodicalId":333742,"journal":{"name":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A message passing algorithm for active contours\",\"authors\":\"Ahmed Kirmani, Naveen Goela, N. Chatterjee, Ben Vigoda\",\"doi\":\"10.1109/ICASSP.2008.4518053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many important early vision techniques, such as active contours (ACs), can be formulated as energy minimization. However, finding global optimum (minimum energy) configurations is often computationally intractable. Approximate solutions obtained using iterative numerical methods may be ill-conditioned, and exhibit poor convergence and inaccuracy due to noise and discretization errors. We formulate AC as a statistical estimation problem and solve it using (Gaussian) message passing on factor graphs of linear models. The resulting algorithm exhibits faster convergence and the solutions possess higher numerical stability, robustness and accuracy.\",\"PeriodicalId\":333742,\"journal\":{\"name\":\"2008 IEEE International Conference on Acoustics, Speech and Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Acoustics, Speech and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.2008.4518053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2008.4518053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Many important early vision techniques, such as active contours (ACs), can be formulated as energy minimization. However, finding global optimum (minimum energy) configurations is often computationally intractable. Approximate solutions obtained using iterative numerical methods may be ill-conditioned, and exhibit poor convergence and inaccuracy due to noise and discretization errors. We formulate AC as a statistical estimation problem and solve it using (Gaussian) message passing on factor graphs of linear models. The resulting algorithm exhibits faster convergence and the solutions possess higher numerical stability, robustness and accuracy.