Issue No. 11 - November (1989 vol. 11)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.42859
<p>The authors describe an algorithm which uses a Gaussian and mean curvature profile for extracting special points on terrain and then use 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 those points in the presence of noise and with resampling is investigated. The input for this algorithm consists of 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 algorithm is tested with and without the presence of noise, and its performance is described.</p>
Gaussian curvature; computerised pattern recognition; 3D digital terrain data; visual navigation; terrain recognition; mean curvature profile; mean curvature image; quadratic surface; square window; surface fitting; computerised navigation; computerised pattern recognition; computerised picture processing
D. Goldgof, T. Huang and H. Lee, "A Curvature-Based Approach to Terrain Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 1213-1217, 1989.