{"title":"基于形态学的形状和图像匹配","authors":"Wei Gong, Qinyun Shi, Minde Cheng","doi":"10.1109/ICPR.1992.201866","DOIUrl":null,"url":null,"abstract":"New measures for shape and image matching error are presented based on morphological method. It is shown that they grasp the structural and distributive information of the error so as to avoid the incorrect decision by classical ways. The properties of the new measures, such as the dominance of large features, are given. The experiments show these measures have a strong ability against noise and can be used effectively in shape recognition and image registration.<<ETX>>","PeriodicalId":34917,"journal":{"name":"模式识别与人工智能","volume":"14 1","pages":"673-676"},"PeriodicalIF":0.0000,"publicationDate":"1992-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Shape and image matching by use of morphology\",\"authors\":\"Wei Gong, Qinyun Shi, Minde Cheng\",\"doi\":\"10.1109/ICPR.1992.201866\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"New measures for shape and image matching error are presented based on morphological method. It is shown that they grasp the structural and distributive information of the error so as to avoid the incorrect decision by classical ways. The properties of the new measures, such as the dominance of large features, are given. The experiments show these measures have a strong ability against noise and can be used effectively in shape recognition and image registration.<<ETX>>\",\"PeriodicalId\":34917,\"journal\":{\"name\":\"模式识别与人工智能\",\"volume\":\"14 1\",\"pages\":\"673-676\"},\"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.1992.201866\",\"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.1992.201866","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
New measures for shape and image matching error are presented based on morphological method. It is shown that they grasp the structural and distributive information of the error so as to avoid the incorrect decision by classical ways. The properties of the new measures, such as the dominance of large features, are given. The experiments show these measures have a strong ability against noise and can be used effectively in shape recognition and image registration.<>
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