View in EDS

Robust synchronization in SO(3) and SE(3) via low-rank and sparse matrix decomposition.

Saved in:
Bibliographic Details
Title: Robust synchronization in SO(3) and SE(3) via low-rank and sparse matrix decomposition.
Authors: Arrigoni, Federica1, Rossi, Beatrice2, Fragneto, Pasqualina2, Fusiello, Andrea1 andrea.fusiello@uniud.it
Source: Computer Vision & Image Understanding. Sep2018, Vol. 174, p95-113. 19p.
Subjects: Sparse matrix software, Synchronization software, Algorithms, Outlier detection, Decomposition method
Abstract: Highlights • Synchronization in SO(3) and SE(3) is formulated as a low-rank and sparse matrix decomposition problem. • Any low-rank and sparse matrix decomposition algorithm can be used in this framework. • Good trade-off between resistance to outliers and speed. Abstract This paper deals with the synchronization problem, which arises in multiple 3D point-set registration and in structure-from-motion. The problem is formulated as a low-rank and sparse matrix decomposition that caters for missing data, outliers and noise, and it benefits from a wealth of available decomposition algorithms that can be plugged-in. A minimization strategy, dubbed R-GoDec , is also proposed. Experimental results on simulated and real data show that this approach offers a good trade-off between resistance to outliers and speed. [ABSTRACT FROM AUTHOR]
Copyright of Computer Vision & Image Understanding is the property of Academic Press Inc. 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
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 133423182
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Robust synchronization in SO(3) and SE(3) via low-rank and sparse matrix decomposition.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Arrigoni%2C+Federica%22">Arrigoni, Federica</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rossi%2C+Beatrice%22">Rossi, Beatrice</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Fragneto%2C+Pasqualina%22">Fragneto, Pasqualina</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Fusiello%2C+Andrea%22">Fusiello, Andrea</searchLink><relatesTo>1</relatesTo><i> andrea.fusiello@uniud.it</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Computer+Vision+%26+Image+Understanding%22">Computer Vision & Image Understanding</searchLink>. Sep2018, Vol. 174, p95-113. 19p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Sparse+matrix+software%22">Sparse matrix software</searchLink><br /><searchLink fieldCode="DE" term="%22Synchronization+software%22">Synchronization software</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Decomposition+method%22">Decomposition method</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights • Synchronization in SO(3) and SE(3) is formulated as a low-rank and sparse matrix decomposition problem. • Any low-rank and sparse matrix decomposition algorithm can be used in this framework. • Good trade-off between resistance to outliers and speed. Abstract This paper deals with the synchronization problem, which arises in multiple 3D point-set registration and in structure-from-motion. The problem is formulated as a low-rank and sparse matrix decomposition that caters for missing data, outliers and noise, and it benefits from a wealth of available decomposition algorithms that can be plugged-in. A minimization strategy, dubbed R-GoDec , is also proposed. Experimental results on simulated and real data show that this approach offers a good trade-off between resistance to outliers and speed. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Vision & Image Understanding is the property of Academic Press Inc. 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=133423182
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.cviu.2018.08.001
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 95
    Subjects:
      – SubjectFull: Sparse matrix software
        Type: general
      – SubjectFull: Synchronization software
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Outlier detection
        Type: general
      – SubjectFull: Decomposition method
        Type: general
    Titles:
      – TitleFull: Robust synchronization in SO(3) and SE(3) via low-rank and sparse matrix decomposition.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Arrigoni, Federica
      – PersonEntity:
          Name:
            NameFull: Rossi, Beatrice
      – PersonEntity:
          Name:
            NameFull: Fragneto, Pasqualina
      – PersonEntity:
          Name:
            NameFull: Fusiello, Andrea
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 10773142
          Numbering:
            – Type: volume
              Value: 174
          Titles:
            – TitleFull: Computer Vision & Image Understanding
              Type: main
ResultId 1