• DocumentCode
    2714153
  • Title

    Fast axis estimation from a segment of rotationally symmetric object

  • Author

    Han, Dongjin ; Cooper, David B. ; Hahn, Hern-soo

  • Author_Institution
    ITRC, Soongsil Univ., Seoul, South Korea
  • fYear
    2012
  • fDate
    16-21 June 2012
  • Firstpage
    1154
  • Lastpage
    1161
  • Abstract
    This paper proposes a new method for estimating the symmetric axis of a pottery from its small fragment using surface geometry. For the automatic assembly of broken sherds, the axis estimation is an important measure [2]. When a fragment is small, it is difficult to estimate axis orientation since it looks like a patch of a sphere and conventional methods mostly fail, but the proposed method provides reliable axis estimation by using multiple constraints. The computational cost is also much lowered. To estimate the symmetric axis, the proposed algorithm uses three constraints: (1) the curvature is constant on a circumference CH. (2) the curvature is invariant in any scale. (3) also the principal curvatures does not vary on CH. CH is a planar circle which is one of all the possible circumferences of a pottery or sherd. A hypothesis test for axis is performed using maximum likelihood. The variance of curvature, multi-scale curvature and principal curvatures are computed in the likelihood function. We also show that the principal curvatures can be used for grouping of sherds. The grouping of sherds will reduce the computation significantly by omitting impossible configurations in pottery assembly.
  • Keywords
    archaeology; curve fitting; image segmentation; maximum likelihood estimation; pottery; automatic assembly; broken sherd; computational cost; curvature variance; likelihood function; maximum likelihood; multiscale curvature; pottery assembly; principal curvature; rotationally symmetric object segmentation; surface geometry; symmetric axis estimation; Assembly; Face; Histograms; Maximum likelihood estimation; Shape; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
  • Conference_Location
    Providence, RI
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4673-1226-4
  • Electronic_ISBN
    1063-6919
  • Type

    conf

  • DOI
    10.1109/CVPR.2012.6247796
  • Filename
    6247796