A Curvature-Based Approach to Terrain Recognition

Dmitry B. Goldgof, Thomas S. Huang, Hua Lee

Research output: Contribution to journalArticlepeer-review


This paper describes an algorithm which uses a Gaussian and mean curvature profile for extracting special points on the terrain, and then uses these points for recognition of particular regions of the terrain. The Gaussian and mean curvatures are chosen because they are invariant under rotation and translation. In the Gaussian and mean curvature image, the points of maximum and minimum curvature are extracted and used for matching. The stability of the position of these points in the presence of noise and with resampling is investigated. The input for this algorithm is 3-D digital terrain data. Curvature values are calculated from the data by fitting a quadratic surface over a square window and calculating directional derivatives of this surface. A method of surface fitting which is invariant to coordinate system transformation is suggested and implemented. The real terrain data used in our experiments are compiled by the U.S. Army Engineer Topographic Laboratories, Fort Belvoir, VA. The algorithm is tested with and without the presence of noise and its performance is described.

Original languageEnglish (US)
Pages (from-to)1213-1217
Number of pages5
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number11
StatePublished - Nov 1989
Externally publishedYes


  • 3-D representation and recognition
  • Computer vision
  • range imaging
  • shape and 3-D description
  • visual navigation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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