Training Binary Descriptors for Improved Robustness and Efficiency in Real-Time Matching View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2013

AUTHORS

Sharat Saurabh Akhoury , Robert Laganière

ABSTRACT

Most descriptor-based keypoint recognition methods require computationally expensive patch preprocessing to obtain insensitivity to various kinds of deformations. This limits their applicability towards real-time applications on low-powered devices such as mobile phones. In this paper, we focus on descriptors which are relatively weak (i.e. sensitive to scale and rotation), and present a classification-based approach to improve their robustness and efficiency to achieve real-time matching. We demonstrate our method by applying it to BRIEF [7] resulting in comparable robustness to SIFT [4], while outperforming several state-of-the-art descriptors like SURF [6], ORB [8], and FREAK [10]. More... »

PAGES

288-298

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1984. The State of the Art in STRATEGIC PLANNING IN NATIONALISED INDUSTRIES
  • 2010. Adaptive and Generic Corner Detection Based on the Accelerated Segment Test in COMPUTER VISION – ECCV 2010
  • 2006. Machine Learning for High-Speed Corner Detection in COMPUTER VISION – ECCV 2006
  • 2008. CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching in COMPUTER VISION – ECCV 2008
  • 2011-09. Binary Histogrammed Intensity Patches for Efficient and Robust Matching in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010. BRIEF: Binary Robust Independent Elementary Features in COMPUTER VISION – ECCV 2010
  • Book

    TITLE

    Image Analysis and Processing – ICIAP 2013

    ISBN

    978-3-642-41183-0
    978-3-642-41184-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-41184-7_30

    DOI

    http://dx.doi.org/10.1007/978-3-642-41184-7_30

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1011956355


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