• DocumentCode
    3672185
  • Title

    Continuous Visibility Feature

  • Author

    Guilin Liu;Yotam Gingold;Jyh-Ming Lien

  • Author_Institution
    Department of Computer Science, George Mason University, Fairfax, VA, USA, 22030
  • fYear
    2015
  • fDate
    6/1/2015 12:00:00 AM
  • Firstpage
    1182
  • Lastpage
    1190
  • Abstract
    In this work, we propose a new type of visibility measurement named Continuous Visibility Feature (CVF). We say that a point q on the mesh is continuously visible from another point p if there exists a geodesic path connecting p and q that is entirely visible by p. In order to efficiently estimate the continuous visibility for all the vertices in a model, we propose two approaches that use specific CVF properties to avoid exhaustive visibility tests. CVF is then measured as the area of the continuously visible region. With this stronger visibility measure, we show that CVF better encodes the surface and part information of mesh than the tradition line-of-sight based visibility. For example, we show that existing segmentation algorithms can generate better segmentation results using CVF and its variants than using other visibility-based shape descriptors, such as shape diameter function. Similar to visibility and other mesh surface features, continuous visibility would have many applications.
  • Keywords
    "Shape","Benchmark testing","Joining processes","TV","Feature extraction","Semantics","Level measurement"
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
  • Electronic_ISBN
    1063-6919
  • Type

    conf

  • DOI
    10.1109/CVPR.2015.7298722
  • Filename
    7298722