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access icon free Efficient methods using slanted support windows for slanted surfaces

  • Author(s): Xuesong Li 1, 2 ; Jianguo Liu 1, 2 ; Guang Chen 1, 2 ; Heng Fu 1, 2
    • View affiliations
    • Affiliations: 1: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, People's Republic of China ;
      2: Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, People's Republic of China
  • Source: Volume 10, Issue 5, August 2016, p. 384 – 391
    DOI:  10.1049/iet-cvi.2015.0106 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Received 27/04/2015, Accepted 09/11/2015, Revised 20/10/2015, Published 14/03/2016

The frontal-parallel assumption is made by many matching algorithms, but this assumption fails for slanted surfaces. This study proposes a matching algorithm intended to improve the matching results for slanted surfaces. First, a mathematical model is constructed to prove that slanted surfaces in the environment have corresponding slanted disparity surfaces in the disparity space image, and the model is to help find the proper plane parameters of slanted support windows, then improved cost aggregation and post-processing methods are proposed. The algorithm is tested using the Middlebury and Karlsruhe Institute of Technology and Toyota Technical Institute at Chicago (KITTI) benchmarks. The results demonstrate that the algorithm exhibits good performance and is efficient for slanted surfaces.

Inspec keywords: optical windows; image matching

Other keywords: KITTI benchmarks; Toyota Technical Institute; matching algorithms; slanted support windows; plane parameters; Middlebury; frontal-parallel assumption; disparity space image; Chicago; Karlsruhe Institute of Technology; slanted disparity surfaces; mathematical model; cost aggregation; post-processing methods; slanted surfaces

Subjects: Computer vision and image processing techniques; Image recognition

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