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Proceedings Paper

Shape metrics from curvature-scale space and curvature-tuned smoothing
Author(s): Gregory Dudek
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Paper Abstract

This research deals with the decomposition and description of curved objects. In ongoing work, a new part description for curves and surfaces using a set of curvature-based minimization operators has been developed. The decomposition operation simultaneously performs data interpolation, data smoothing, and segmentation. The unification of these three stages results in a smoothing operation that is tightly coupled with the primitives to be used in subsequent object description. Each of the minimization operators, in addition to having a curvature tuning, has a different spatial sensitivity function. As a result, different possible descriptions of an object are produced and these capture information at multiple spatial scales. Each object is described by a small number of tokens based on differential geometric properties. The set of descriptors produced for a given object can be organized into an unusual form of nonlinear scale-space. The utility of such a scale-based description by way of two methods for the characterization (i.e., recognition) of two-dimensional objects via their multi- scale signature in terms of curvature-scale-space features is demonstrated. One method is based on graph matching by dynamic programming and the other based on statistical properties of scale space ('shape texture').

Paper Details

Date Published: 1 September 1991
PDF: 11 pages
Proc. SPIE 1570, Geometric Methods in Computer Vision, (1 September 1991); doi: 10.1117/12.49976
Show Author Affiliations
Gregory Dudek, McGill Univ. (Canada)

Published in SPIE Proceedings Vol. 1570:
Geometric Methods in Computer Vision
Baba C. Vemuri, Editor(s)

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