{"title":"一种新的三维形状描述符计算框架","authors":"C.B. Akgul, B. Sankur, Y. Yemez, F. Schmitt","doi":"10.1109/SIU.2006.1659825","DOIUrl":null,"url":null,"abstract":"In this work, we propose a computational framework for histogram-based 3D shape descriptors. Our method is based on evaluating the density of a shape function defined over the surface of 3D model using Gaussian modeling. The proposed approach has a better shape description ability compared to other competitor histogram-based approaches. We illustrate this assertion in a content-based 3D model retrieval application","PeriodicalId":415037,"journal":{"name":"2006 IEEE 14th Signal Processing and Communications Applications","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Computational Framework for 3D Shape Descriptors\",\"authors\":\"C.B. Akgul, B. Sankur, Y. Yemez, F. Schmitt\",\"doi\":\"10.1109/SIU.2006.1659825\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we propose a computational framework for histogram-based 3D shape descriptors. Our method is based on evaluating the density of a shape function defined over the surface of 3D model using Gaussian modeling. The proposed approach has a better shape description ability compared to other competitor histogram-based approaches. We illustrate this assertion in a content-based 3D model retrieval application\",\"PeriodicalId\":415037,\"journal\":{\"name\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"volume\":\"121 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2006.1659825\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 14th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2006.1659825","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Computational Framework for 3D Shape Descriptors
In this work, we propose a computational framework for histogram-based 3D shape descriptors. Our method is based on evaluating the density of a shape function defined over the surface of 3D model using Gaussian modeling. The proposed approach has a better shape description ability compared to other competitor histogram-based approaches. We illustrate this assertion in a content-based 3D model retrieval application
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