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Proceedings Paper

Multiscale discretization of shape contours
Author(s): Lakshman Prasad; Ramana L. Rao
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Paper Abstract

We present an efficient multi-scale shape approximation scheme by adaptively and sparsely discretizing its continuous (or densely sampled) contour by means of points. The notion of shape is intimately related to the notion of contour and, therefore, the efficient representation of the contour of a shape is vital to a computational understanding of the shape. Any discretization of a planar smooth curve by points is equivalent to a piecewise constant approximation of its parameterized X and Y coordinate. Using the Haar wavelet transform for the piecewise approximation yields a hierarchical scheme in which the size of the approximating point set is traded off against the morphological accuracy of the approximation. Our algorithm compresses the representation of the initial shape contour to a sparse sequence of points in the plane defining the vertices of the shape's polygonal approximation. Furthermore, it is possible to control the overall resolution of the approximation by a single, scale- independent parameter.

Paper Details

Date Published: 23 October 2000
PDF: 8 pages
Proc. SPIE 4117, Vision Geometry IX, (23 October 2000); doi: 10.1117/12.404822
Show Author Affiliations
Lakshman Prasad, Los Alamos National Lab. (United States)
Ramana L. Rao, Integrated Circuit Technology Corp. (United States)

Published in SPIE Proceedings Vol. 4117:
Vision Geometry IX
Longin Jan Latecki; David M. Mount; Angela Y. Wu, Editor(s)

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