{"title":"一种基于内容的三维医学模型局部特征提取方法","authors":"L. Bergamasco, Fátima L. S. Nunes","doi":"10.1145/2554850.2554873","DOIUrl":null,"url":null,"abstract":"Three-dimensional models are being extensively used in our society. Global shape descriptors are more frequently used in the Content-Based Image Retrieval (CBIR) context due to their robustness and easy implementation, but this kind of descriptor is not adequate for retrieval models with specific characteristics. In this paper a local descriptor is proposed, which analyzes the 3D model shape in different locations of the object in order to increase the retrieval accuracy. Our method is compared with a global descriptor, Distance Histogram, using generic models and specific models which have shape deformations in specific areas. Results show that our method presented higher performance in both contexts.","PeriodicalId":285655,"journal":{"name":"Proceedings of the 29th Annual ACM Symposium on Applied Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A new local feature extraction approach for content-based 3D medical model retrieval using shape descriptor\",\"authors\":\"L. Bergamasco, Fátima L. S. Nunes\",\"doi\":\"10.1145/2554850.2554873\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Three-dimensional models are being extensively used in our society. Global shape descriptors are more frequently used in the Content-Based Image Retrieval (CBIR) context due to their robustness and easy implementation, but this kind of descriptor is not adequate for retrieval models with specific characteristics. In this paper a local descriptor is proposed, which analyzes the 3D model shape in different locations of the object in order to increase the retrieval accuracy. Our method is compared with a global descriptor, Distance Histogram, using generic models and specific models which have shape deformations in specific areas. Results show that our method presented higher performance in both contexts.\",\"PeriodicalId\":285655,\"journal\":{\"name\":\"Proceedings of the 29th Annual ACM Symposium on Applied Computing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 29th Annual ACM Symposium on Applied Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2554850.2554873\",\"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 the 29th Annual ACM Symposium on Applied Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2554850.2554873","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new local feature extraction approach for content-based 3D medical model retrieval using shape descriptor
Three-dimensional models are being extensively used in our society. Global shape descriptors are more frequently used in the Content-Based Image Retrieval (CBIR) context due to their robustness and easy implementation, but this kind of descriptor is not adequate for retrieval models with specific characteristics. In this paper a local descriptor is proposed, which analyzes the 3D model shape in different locations of the object in order to increase the retrieval accuracy. Our method is compared with a global descriptor, Distance Histogram, using generic models and specific models which have shape deformations in specific areas. Results show that our method presented higher performance in both contexts.