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C-SURF: Colored Speeded Up Robust Features

  • Jing Fu
  • Xiaojun Jing
  • Songlin Sun
  • Yueming Lu
  • Ying Wang
Part of the Communications in Computer and Information Science book series (CCIS, volume 320)

Abstract

SURF has been proven to be one of the state-of-the art feature detector and descriptor, and mainly treats colorful images as gray images. However, color provides valuable information in the object description and recognition tasks. This paper addresses this problem and adds the color information into the scale-and rotation-invariant interest point detector and descriptor, coined C-SURF (Colored Speeded Up Robust Features). The built C-SURF is more robust than the conventional SURF with respect to rotation variations. Moreover, we use 112 dimensions to describe not only the distribution of Harr-wavelet responses but also the color information within the interest point neighborhood. The evaluation results support the potential of the proposed approach.

Keywords

object recognition local invariant features SURF color images 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jing Fu
    • 1
    • 2
  • Xiaojun Jing
    • 1
    • 2
  • Songlin Sun
    • 1
    • 2
  • Yueming Lu
    • 1
    • 2
  • Ying Wang
    • 1
    • 2
  1. 1.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)Ministry of EducationChina

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