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Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
January 1986 (vol. 8 no. 1)
pp. 34-43
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.
Citation:
Farzin Mokhtarian, Alan Mackworth, "Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 1, pp. 34-43, Jan. 1986, doi:10.1109/TPAMI.1986.4767750
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