{"title":"基于特征树的快速三维物体识别","authors":"O. Munkelt, Christoph Zierl","doi":"10.1109/ICPR.1994.576470","DOIUrl":null,"url":null,"abstract":"This contribution focuses on the recognition of a priori known 3-D objects in single 2-D images. The underlying model is embedded in the domain of CAD-based vision using a viewer-centered approach to generate a set of normalized views. They serve as a basis for an optimal selection of properties of features. The idea of an aspect is used for grouping the values of the properties into aspect-trees. The aim of this approach is to identify fast the correct view of an object seen in the image and thereby to distinguish between different objects. Various experiments with different objects and under stepwise varied conditions clearly demonstrate the robustness of this approach and its ability of recognizing 3-D objects.","PeriodicalId":312019,"journal":{"name":"Proceedings of 12th International Conference on Pattern Recognition","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Fast 3-D object recognition using feature based aspect-trees\",\"authors\":\"O. Munkelt, Christoph Zierl\",\"doi\":\"10.1109/ICPR.1994.576470\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This contribution focuses on the recognition of a priori known 3-D objects in single 2-D images. The underlying model is embedded in the domain of CAD-based vision using a viewer-centered approach to generate a set of normalized views. They serve as a basis for an optimal selection of properties of features. The idea of an aspect is used for grouping the values of the properties into aspect-trees. The aim of this approach is to identify fast the correct view of an object seen in the image and thereby to distinguish between different objects. Various experiments with different objects and under stepwise varied conditions clearly demonstrate the robustness of this approach and its ability of recognizing 3-D objects.\",\"PeriodicalId\":312019,\"journal\":{\"name\":\"Proceedings of 12th International Conference on Pattern Recognition\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 12th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1994.576470\",\"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 of 12th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1994.576470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast 3-D object recognition using feature based aspect-trees
This contribution focuses on the recognition of a priori known 3-D objects in single 2-D images. The underlying model is embedded in the domain of CAD-based vision using a viewer-centered approach to generate a set of normalized views. They serve as a basis for an optimal selection of properties of features. The idea of an aspect is used for grouping the values of the properties into aspect-trees. The aim of this approach is to identify fast the correct view of an object seen in the image and thereby to distinguish between different objects. Various experiments with different objects and under stepwise varied conditions clearly demonstrate the robustness of this approach and its ability of recognizing 3-D objects.