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Proceedings Paper

Digitizations preserving topological and differential geometric properties
Author(s): Ari David Gross; Longin Jan Latecki
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Paper Abstract

In this paper we present conditions which guarantee that every digitization process preserves important topological and differential geometric properties. These conditions also allow us to determine the correct digitization resolution for a given class of real objects. Knowing that these properties are invariant under digitization, we can then use them in feature-based recognition. Moreover, these conditions imply that only a few digital patterns can occur as neighborhoods of boundary points in the digitization. This is very useful for noise detection, since if the neighborhood of a boundary point does not match one of these patterns, it must be due to noise. Our definition of a digitization approximates many real digitization processes. The digitization process is modeled as a mapping from continuous sets representing real objects to discrete sets represented as digital images. We show that an object A and the digitization of A are homotopy equivalent. This, for example, implies that the digitization of A preserves connectivity of the object and its complement. Moreover, we show that the digitization of A will not change the qualitative differential geometric properties of the boundary of A, i.e. a boundary point which is locally convex cannot be digitized to a locally concave pixel and a boundary point which is locally concave cannot be digitized to a locally convex pixel.

Paper Details

Date Published: 4 January 1995
PDF: 12 pages
Proc. SPIE 2356, Vision Geometry III, (4 January 1995); doi: 10.1117/12.198621
Show Author Affiliations
Ari David Gross, CUNY/Queens College (United States)
Longin Jan Latecki, Univ. of Hamburg (United States)

Published in SPIE Proceedings Vol. 2356:
Vision Geometry III
Robert A. Melter; Angela Y. Wu, Editor(s)

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