• DocumentCode
    1043371
  • Title

    Acquiring 3-D models from sequences of contours

  • Author

    Zheng, Jiang Yu

  • Author_Institution
    Fac. of Comput. Sci. & Syst. Eng., Kyushu Inst. of Technol., Fukuoka, Japan
  • Volume
    16
  • Issue
    2
  • fYear
    1994
  • fDate
    2/1/1994 12:00:00 AM
  • Firstpage
    163
  • Lastpage
    178
  • Abstract
    This paper explores shape from contour for acquiring 3-D graphics models. In this method, a continuous sequence of images is taken as an object rotates. A smooth convex shape can be estimated instantaneously from its contour and by the first derivative of contour movement (trace of contour, or contour distribution with time). We also analyze shapes that do not satisfy the conditions of smoothness and visibility, which are indispensable for modeling an object. A region that does not expose as contour yields a nonsmoothness in the tracked contour movement. We can thus detect such a region by contour distribution filtering and extract its accurate location by computing the left and right derivatives of the distribution. This has not been studied previously. These unknown regions are obtained for further investigation using other visual cues. A general approach for building a geometrical object model using contours is then described. The entire process from silhouettes to a 3-D model is based local computation; this is promising for producing shapes in real time. Our direct goal is to establish 3-D graphics models of human faces for the growing needs of visual communications. We have obtained some good results
  • Keywords
    filtering and prediction theory; image sequences; 3-D graphics models acquisition; contour distribution filtering; contour sequences; geometrical object; image sequence; shape from contour; smooth convex shape; tracked contour movement; Face; Filtering; Graphics; Humans; Image sequences; Lighting; Reflectivity; Shape measurement; Surface emitting lasers; Tracking;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.273734
  • Filename
    273734