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Rapid Triangle Matching Based on Binary Descriptors

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Book cover Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

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Abstract

Geometric constraints have been widely applied to image matching to gain additional advantages over feature points. A rapid triangle matching (RTM) algorithm was such an algorithm for matching triangles formed by three feature points using 38-dimensional floating-point descriptors. The RTM was faster than SIFT, but it was still hard to meet the real-time requirement. As such, we designed two kinds of binary descriptors including FREAK and rBRIEF used in ORB to replace the floating-point descriptors of RTM, and compared the improved RTM algorithms with original RTM algorithm and SIFT based on simulated and actual binocular images. The results demonstrate that our algorithms greatly improve the speed as well as the precision and matching score. Furthermore, our algorithms can match additional points compared to SIFT and the original RTM when applied to structural scenes.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116023, China

    Min Tian & Qiu-Hua Lin

Authors
  1. Min Tian
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  2. Qiu-Hua Lin
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Correspondence to Qiu-Hua Lin .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Tian, M., Lin, QH. (2017). Rapid Triangle Matching Based on Binary Descriptors. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_40

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

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