{"title":"结合距离和强度数据与隐马尔可夫模型","authors":"R. B. Huseby, G. Høgåsen, G. Storvik, K. Aas","doi":"10.1109/ICPR.1992.201737","DOIUrl":null,"url":null,"abstract":"The paper treats the analysis of an industrial inspection problem, namely the segmentation and discrimination of similar-looking bottles based on a multispectral image consisting of both range and intensity data. A contextual pixel classification is performed using a whole line as neighborhood. The framework of hidden Markov models together with a fast algorithm from control engineering makes this possible. The method is compared to J. Haslett's method (1985) for contextual classification, and performs significantly better.<<ETX>>","PeriodicalId":34917,"journal":{"name":"模式识别与人工智能","volume":"1 1","pages":"128-131"},"PeriodicalIF":0.0000,"publicationDate":"1992-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Combining range and intensity data with a hidden Markov model\",\"authors\":\"R. B. Huseby, G. Høgåsen, G. Storvik, K. Aas\",\"doi\":\"10.1109/ICPR.1992.201737\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper treats the analysis of an industrial inspection problem, namely the segmentation and discrimination of similar-looking bottles based on a multispectral image consisting of both range and intensity data. A contextual pixel classification is performed using a whole line as neighborhood. The framework of hidden Markov models together with a fast algorithm from control engineering makes this possible. The method is compared to J. Haslett's method (1985) for contextual classification, and performs significantly better.<<ETX>>\",\"PeriodicalId\":34917,\"journal\":{\"name\":\"模式识别与人工智能\",\"volume\":\"1 1\",\"pages\":\"128-131\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"模式识别与人工智能\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1992.201737\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"模式识别与人工智能","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICPR.1992.201737","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
Combining range and intensity data with a hidden Markov model
The paper treats the analysis of an industrial inspection problem, namely the segmentation and discrimination of similar-looking bottles based on a multispectral image consisting of both range and intensity data. A contextual pixel classification is performed using a whole line as neighborhood. The framework of hidden Markov models together with a fast algorithm from control engineering makes this possible. The method is compared to J. Haslett's method (1985) for contextual classification, and performs significantly better.<>
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