IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-319-72745-5_34.html
   My bibliography  Save this book chapter

A Local Neighborhood Constraint Method for SIFT Features Matching

In: Recent Developments in Data Science and Business Analytics

Author

Listed:
  • Qingliang Li

    (Changchun University of Science and Technology, School of Computer Science and Technology)

  • Lili Xu

    (Changchun University of Science and Technology, School of Computer Science and Technology)

  • Pengliang Zheng

    (Changchun University of Science and Technology, School of Computer Science and Technology)

  • Fei He

    (Changchun University of Science and Technology, School of Computer Science and Technology)

Abstract

For improving the accuracy of the SIFT matching algorithm with low time cost, this paper proposes a novel matching algorithm which is based on local neighborhood constraints, that is, SIFT matching feature is optimized by the local neighborhood constraint method in the SIFT algorithm. We optimize the matching results by using the information of SIFT feature descriptor and the relative position information of SIFT feature, then the final matching result obtained by RANSANC algorithm to filter the false matched pairs. The experimental results show that our method can improve the accuracy of the matching feature pairs without affecting the time cost.

Suggested Citation

  • Qingliang Li & Lili Xu & Pengliang Zheng & Fei He, 2018. "A Local Neighborhood Constraint Method for SIFT Features Matching," Springer Proceedings in Business and Economics, in: Madjid Tavana & Srikanta Patnaik (ed.), Recent Developments in Data Science and Business Analytics, chapter 0, pages 313-320, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-72745-5_34
    DOI: 10.1007/978-3-319-72745-5_34
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-319-72745-5_34. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.