Issue No. 01 - January (1986 vol. 8)
Farzin Mokhtarian , Laboratory for Computational Vision, Department of Computer Science, University of British Columbia, Vancouver, B.C., V6T 1W5, Canada.
Alan Mackworth , Laboratory for Computational Vision, Department of Computer Science, University of British Columbia, Vancouver, B.C., V6T 1W5, Canada.
The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved in this paper. A number of necessary criteria are imposed on any candidate solution method. Path-based Gaussian smoothing techniques are applied to the curve to find zeros of curvature at varying levels of detail. The result is the ``generalized scale space'' image of a planar curve which is invariant under rotation, uniform scaling and translation of the curve. These properties make the scale space image suitable for matching. The matching algorithm is a modification of the uniform cost algorithm and finds the lowest cost match of contours in the scale space images. It is argued that this is preferable to matching in a so-called stable scale of the curve because no such scale may exist for a given curve. This technique is applied to register a Landsat satellite image of the Strait of Georgia, B.C. (manually corrected for skew) to a map containing the shorelines of an overlapping area.
F. Mokhtarian and A. Mackworth, "Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 8, no. , pp. 34-43, 1986.