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A statistical approach for extraction of feature lines from point clouds.

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Title: A statistical approach for extraction of feature lines from point clouds.
Authors: Zhang, Yuhe1 zhangyuhe0601@126.com, Geng, Guohua1 ghgeng@nwu.edu.cn, Wei, Xiaoran1 bloodyfeline@163.com, Zhang, Shunli1 slzhang@nwu.edu.cn, Li, Shanshan1 ssl0217@163.com
Source: Computers & Graphics. May2016, Vol. 56, p31-45. 15p.
Subjects: Feature extraction, Computer drawing software, Cloud computing, Poisson distribution, Statistical sampling
Abstract: This paper firstly introduces the use of a statistical model based on the Poisson distribution as a tool for extracting feature points from point clouds. When the features on a model are non-uniform or the surfaces are not completely smooth, the thresholds for different local features must be set differently. Such case is ill-suited to all previous global fixed thresholds dependent methods. The Poisson distribution based method is preferable because it computes different thresholds for different local features adaptively depending on the natural properties of the surfaces. Secondly, the region information analysis is employed to cluster the border/edge points, identify the corner points and acquire the link information for feature lines. Finally, the complete feature lines are reconstructed based on the geometry representations of the border point clusters that are created by adapting L 1 -median locally, instead of using distance/path parameters. The proposed method does not need any prior surface reconstruction and is not largely affected by noisy points, neighborhood scales or sampling quality. We demonstrate the benefits of our method with the favorable results for real-world point clouds with varying types of features and the application of the generated feature lines to the computer aided line-drawings generation of Terracotta. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Graphics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 114697126
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A statistical approach for extraction of feature lines from point clouds.
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Graphics%22">Computers & Graphics</searchLink>. May2016, Vol. 56, p31-45. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+drawing+software%22">Computer drawing software</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Poisson+distribution%22">Poisson distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This paper firstly introduces the use of a statistical model based on the Poisson distribution as a tool for extracting feature points from point clouds. When the features on a model are non-uniform or the surfaces are not completely smooth, the thresholds for different local features must be set differently. Such case is ill-suited to all previous global fixed thresholds dependent methods. The Poisson distribution based method is preferable because it computes different thresholds for different local features adaptively depending on the natural properties of the surfaces. Secondly, the region information analysis is employed to cluster the border/edge points, identify the corner points and acquire the link information for feature lines. Finally, the complete feature lines are reconstructed based on the geometry representations of the border point clusters that are created by adapting L 1 -median locally, instead of using distance/path parameters. The proposed method does not need any prior surface reconstruction and is not largely affected by noisy points, neighborhood scales or sampling quality. We demonstrate the benefits of our method with the favorable results for real-world point clouds with varying types of features and the application of the generated feature lines to the computer aided line-drawings generation of Terracotta. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computers & Graphics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.cag.2016.01.004
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 31
    Subjects:
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Computer drawing software
        Type: general
      – SubjectFull: Cloud computing
        Type: general
      – SubjectFull: Poisson distribution
        Type: general
      – SubjectFull: Statistical sampling
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      – TitleFull: A statistical approach for extraction of feature lines from point clouds.
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            NameFull: Zhang, Yuhe
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            NameFull: Geng, Guohua
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            NameFull: Wei, Xiaoran
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            NameFull: Zhang, Shunli
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            NameFull: Li, Shanshan
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              M: 05
              Text: May2016
              Type: published
              Y: 2016
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              Value: 56
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