{"title":"Fast multiple shape correspondence by pre-organizing shape instances","authors":"B. Munsell, Andrew Temlyakov, Song Wang","doi":"10.1109/cvpr.2009.5206611","DOIUrl":null,"url":null,"abstract":"Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general, shape-correspondence methods can be grouped into one of two categories: global methods and pair-wise methods. In this paper, we develop a new method that attempts to address the limitations of both the global and pair-wise methods. In particular, we reorganize the input population into a tree structure that incorporates global information about the population of shape instances, where each node in the tree represents a shape instance and each edge connects two very similar shape instances. Using this organized tree, neighboring shape instances can be corresponded efficiently and accurately by a pair-wise method. In the experiments, we evaluate the proposed method and compare its performance to five available shape correspondence methods and show the proposed method achieves the accuracy of a global method with speed of a pair-wise method.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Conference on Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/cvpr.2009.5206611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general, shape-correspondence methods can be grouped into one of two categories: global methods and pair-wise methods. In this paper, we develop a new method that attempts to address the limitations of both the global and pair-wise methods. In particular, we reorganize the input population into a tree structure that incorporates global information about the population of shape instances, where each node in the tree represents a shape instance and each edge connects two very similar shape instances. Using this organized tree, neighboring shape instances can be corresponded efficiently and accurately by a pair-wise method. In the experiments, we evaluate the proposed method and compare its performance to five available shape correspondence methods and show the proposed method achieves the accuracy of a global method with speed of a pair-wise method.