• DocumentCode
    2539763
  • Title

    Using three-dimensional features to improve terrain classification

  • Author

    Wang, Xiaoguang ; Stolle, Frank ; Schultz, Howard ; Riseman, Edward M. ; Hanson, Allen R.

  • Author_Institution
    Dept. of Comput. Sci., Massachusetts Univ., Amherst, MA, USA
  • fYear
    1997
  • fDate
    17-19 Jun 1997
  • Firstpage
    915
  • Lastpage
    920
  • Abstract
    Texture has long been regarded as spatial distributions of gray-level variation, and texture analysis has generally been confined to the 2-D image domain. Introducing the concept of “3-D world feature”, this paper considers texture as a function of 3-D structures and proposes a set of “3-D textural features”. The proposed 3-D features appear to have a great potential in terrain classification. Experiments have been carried out to compare the 3-D features with a popular traditional 2-D feature set. The results show that the 3-D features significantly outperform the 2-D features in terms of classification accuracy
  • Keywords
    computer vision; geophysical techniques; image texture; 3-D world feature; classification accuracy; gray-level variation; spatial distributions; terrain classification; texture analysis; three-dimensional features; Cameras; Feature extraction; Image analysis; Image texture; Image texture analysis; Optical sensors; Rough surfaces; Signal processing algorithms; Surface roughness; Surface texture;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
  • Conference_Location
    San Juan
  • ISSN
    1063-6919
  • Print_ISBN
    0-8186-7822-4
  • Type

    conf

  • DOI
    10.1109/CVPR.1997.609437
  • Filename
    609437