MULTISCALE DISCRETIZATION OF SHAPE CONTOURS

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We present an efficient multi-scale scheme to adaptively approximate the continuous (or densely sampled) contour of a planar shape at varying resolutions. The notion of shape is intimately related to the notion of contour, and the efficient representation of the contour of a shape is vital to a computational understanding of the shape. Any polygonal approximation of a planar smooth curve is equivalent to a piecewise constant approximation of the parameterized X and Y coordinate functions of a discrete point set obtained by densely sampling the curve. Using the Haar wavelet transform for the piecewise approximation yields a hierarchical scheme ... continued below

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10 p.

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Prasad, L. & Rao, R. September 1, 2000.

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Description

We present an efficient multi-scale scheme to adaptively approximate the continuous (or densely sampled) contour of a planar shape at varying resolutions. The notion of shape is intimately related to the notion of contour, and the efficient representation of the contour of a shape is vital to a computational understanding of the shape. Any polygonal approximation of a planar smooth curve is equivalent to a piecewise constant approximation of the parameterized X and Y coordinate functions of a discrete point set obtained by densely sampling the curve. 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.

Physical Description

10 p.

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OSTI as DE00764071

Medium: P; Size: 10 pages

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  • SPIE, SAN DIEGO, CA (US), 07/30/2000--08/04/2000

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  • Report No.: LA-UR-00-2852
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 764071
  • Archival Resource Key: ark:/67531/metadc723285

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  • September 1, 2000

Added to The UNT Digital Library

  • Sept. 29, 2015, 5:31 a.m.

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  • April 6, 2017, 6:48 p.m.

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Prasad, L. & Rao, R. MULTISCALE DISCRETIZATION OF SHAPE CONTOURS, article, September 1, 2000; New Mexico. (digital.library.unt.edu/ark:/67531/metadc723285/: accessed October 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.