ABFT: Anisotropic binary feature transform based on structure tensor space

Seungryong Kim, Hunjae Yoo, Seungchul Ryu, Bumsub Ham, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Local feature matching is a fundamental step for many computer vision applications. Recently, binary feature transforms have been popularly proposed to improve the computational efficiency while preserving high matching performance. However, it is sensitive to noise and geometrical distortion such as affine transformation. In this paper, we propose ABFT framework, composed of a noise robust feature detection and affine invariant binary feature description based on a structure tensor space. Experimental results show that ABFT outperforms other state-of-the-art feature transforms in terms of the repeatability, recognition rate, and computational time.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages2920-2923
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Kim, S., Yoo, H., Ryu, S., Ham, B., & Sohn, K. (2013). ABFT: Anisotropic binary feature transform based on structure tensor space. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 2920-2923). [6738601] (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings). https://doi.org/10.1109/ICIP.2013.6738601