{"title":"利用模糊形态学和规则对雷达场景进行描述","authors":"J. Keller, P. Gader, Xiaomei Wang","doi":"10.1109/CVBVS.1999.781101","DOIUrl":null,"url":null,"abstract":"This paper presents a method for automatically generating descriptions of scenes represented by digital images acquired using laser radar (LADAR). A method for matching the scenes to linguistic descriptions is also presented. Both methods rely on fuzzy spatial relations. Primitive spatial relations between objects are computed using fuzzy mathematical morphology and compared to a previous method based on training a neural network to learn human preferences. For each pair of objects in a scene, the primitive spatial relations are combined into complex spatial relations using a fuzzy rule base. A scene description is generated using the highest confidence rule outputs. Scene matching is performed using the outputs of the rules that correspond to the linguistic description. The results show that seemingly significant differences in spatial relationship definitions have little impact on system performance and that reasonable match scores and descriptions can be generated from the fuzzy system.","PeriodicalId":394469,"journal":{"name":"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS'99)","volume":"16 9","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"LADAR scene description using fuzzy morphology and rules\",\"authors\":\"J. Keller, P. Gader, Xiaomei Wang\",\"doi\":\"10.1109/CVBVS.1999.781101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a method for automatically generating descriptions of scenes represented by digital images acquired using laser radar (LADAR). A method for matching the scenes to linguistic descriptions is also presented. Both methods rely on fuzzy spatial relations. Primitive spatial relations between objects are computed using fuzzy mathematical morphology and compared to a previous method based on training a neural network to learn human preferences. For each pair of objects in a scene, the primitive spatial relations are combined into complex spatial relations using a fuzzy rule base. A scene description is generated using the highest confidence rule outputs. Scene matching is performed using the outputs of the rules that correspond to the linguistic description. The results show that seemingly significant differences in spatial relationship definitions have little impact on system performance and that reasonable match scores and descriptions can be generated from the fuzzy system.\",\"PeriodicalId\":394469,\"journal\":{\"name\":\"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS'99)\",\"volume\":\"16 9\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS'99)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVBVS.1999.781101\",\"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 IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS'99)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVBVS.1999.781101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
LADAR scene description using fuzzy morphology and rules
This paper presents a method for automatically generating descriptions of scenes represented by digital images acquired using laser radar (LADAR). A method for matching the scenes to linguistic descriptions is also presented. Both methods rely on fuzzy spatial relations. Primitive spatial relations between objects are computed using fuzzy mathematical morphology and compared to a previous method based on training a neural network to learn human preferences. For each pair of objects in a scene, the primitive spatial relations are combined into complex spatial relations using a fuzzy rule base. A scene description is generated using the highest confidence rule outputs. Scene matching is performed using the outputs of the rules that correspond to the linguistic description. The results show that seemingly significant differences in spatial relationship definitions have little impact on system performance and that reasonable match scores and descriptions can be generated from the fuzzy system.