• DocumentCode
    1473653
  • Title

    Fast semi-global stereo matching via extracting disparity candidates from region boundaries

  • Author

    Chen, Weijie ; Zhang, M.-J. ; Xiong, Z.-H.

  • Author_Institution
    Coll. of Inf. Syst. & Manage., Nat. Univ. of Defense Technol., Changsha, China
  • Volume
    5
  • Issue
    2
  • fYear
    2011
  • fDate
    3/1/2011 12:00:00 AM
  • Firstpage
    143
  • Lastpage
    150
  • Abstract
    This study proposes a novel fast stereo matching algorithm via semi-global energy optimisation, which achieves a considerable improvement in efficiency for just a small price in accuracy. Based on some assumptions, the authors discover that at most two disparity candidates for each scanline segment of reference image can be extracted. With this observation, the authors present a disparity candidate extraction algorithm. This algorithm constructs an energy function based on colour consistency and restrictions between region boundaries. In this approach, the energy function is optimised via the graph-cuts technique, and the pixels involved are only those positioned on region boundaries, which results in greatly reduced vertex number in the constructed graph and subsequently improved efficiency. After that, a simple partial occlusions handling is conducted as a post-processing to enhance the accuracy of the final disparity map, by selecting a right disparity for each segment from extracted candidates. The performances of our method are demonstrated by experiments on the Middlebury test set.
  • Keywords
    feature extraction; graph theory; image colour analysis; image matching; optimisation; stereo image processing; Middlebury test set; colour consistency; constructed graph; disparity candidate extraction algorithm; energy function; extracting disparity candidates; final disparity map; graph-cuts technique; partial occlusions handling; reference image; region boundary; scanline segment; semi-global energy optimisation; semi-global stereo matching; stereo matching algorithm; vertex number;
  • fLanguage
    English
  • Journal_Title
    Computer Vision, IET
  • Publisher
    iet
  • ISSN
    1751-9632
  • Type

    jour

  • DOI
    10.1049/iet-cvi.2009.0105
  • Filename
    5732747