• DocumentCode
    2565723
  • Title

    Automatic 3D ear reconstruction based on binocular stereo vision

  • Author

    Zeng, Hui ; Mu, Zhi-Chun ; Wang, Kai ; Sun, Chao

  • Author_Institution
    Sch. of Inf. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
  • fYear
    2009
  • fDate
    11-14 Oct. 2009
  • Firstpage
    5205
  • Lastpage
    5208
  • Abstract
    This paper presents an automatic 3D ear reconstruction method based on binocular stereo vision. At first, we calibrate the stereo vision system by Zhang´s method. Then the quasi-dense matching method is performed. We use SIFT feature based matching approach and the coarse to fine strategy to compute the seed matches. The adapted match propagation algorithm with known epipolar geometry constraint is used for obtain quasi-dense correspondence points. Finally the 3D ear model can be reconstructed by triangulation and the results are optimized using the bundle adjustment technique. Extensive experimental results have shown that our proposed method can obtain denser 3D ear model than Liu´s multi-view based method. It can get sufficient 3D ear points with lower cost and higher efficiency.
  • Keywords
    computational geometry; feature extraction; image matching; solid modelling; stereo image processing; 3D ear model; SIFT feature based matching approach; automatic 3D ear reconstruction; binocular stereo vision; epipolar geometry constraint; match propagation algorithm; quasi dense correspondence point; quasi dense matching method; Biometrics; Cybernetics; Ear; Geometry; Image recognition; Image reconstruction; Reconstruction algorithms; Shape; Stereo vision; USA Councils; 3D Ear reconstrction; SIFT; bundle adjustment; propagation; quasi-dense matching; triangulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
  • Conference_Location
    San Antonio, TX
  • ISSN
    1062-922X
  • Print_ISBN
    978-1-4244-2793-2
  • Electronic_ISBN
    1062-922X
  • Type

    conf

  • DOI
    10.1109/ICSMC.2009.5345989
  • Filename
    5345989