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

  • Sharat Saurabh Akhoury
  • Robert Laganière
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

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].

Keywords

keypoint recognition feature matching object detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sharat Saurabh Akhoury
    • 1
  • Robert Laganière
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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