{"title":"通用贝叶斯网络视觉系统中任务特定的实用程序","authors":"R. Rimey, C. Brown","doi":"10.1109/CVPR.1992.223214","DOIUrl":null,"url":null,"abstract":"TEA is a task-oriented computer vision system that uses Bayes nets and a maximum expected-utility decision rule to choose a sequence of task-dependent and opportunistic visual operations on the basis of their cost and (present and future) benefit. The authors discuss technical problems regarding utilities, present TEA-1's utility function (which approximates a two-step lookahead), and compare it to various simpler utility functions in experiments with real and simulated scenes.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Task-specific utility in a general Bayes net vision system\",\"authors\":\"R. Rimey, C. Brown\",\"doi\":\"10.1109/CVPR.1992.223214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"TEA is a task-oriented computer vision system that uses Bayes nets and a maximum expected-utility decision rule to choose a sequence of task-dependent and opportunistic visual operations on the basis of their cost and (present and future) benefit. The authors discuss technical problems regarding utilities, present TEA-1's utility function (which approximates a two-step lookahead), and compare it to various simpler utility functions in experiments with real and simulated scenes.<<ETX>>\",\"PeriodicalId\":325476,\"journal\":{\"name\":\"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPR.1992.223214\",\"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 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.1992.223214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Task-specific utility in a general Bayes net vision system
TEA is a task-oriented computer vision system that uses Bayes nets and a maximum expected-utility decision rule to choose a sequence of task-dependent and opportunistic visual operations on the basis of their cost and (present and future) benefit. The authors discuss technical problems regarding utilities, present TEA-1's utility function (which approximates a two-step lookahead), and compare it to various simpler utility functions in experiments with real and simulated scenes.<>
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