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Proceedings Paper

Fast nonparametric detection of regular structure using digital geometric primitives
Author(s): Ari David Gross; Ruben Lusinyants
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Paper Abstract

One of the important problems related to image classification and compression is finding repeated structures in the image. This problem of finding regular structure is especially important with respect to document image analysis, where mismatches necessitate a residual map for symbolic compression. Although the focus of this paper is developing digital geometric models and methods for finding regular structure in digital document images, the applicability of the digital geometric approach is also demonstrated on images taken under affine and perspective projection. First, a fast linear-time algorithm is given to compute the static threshold that minimizes the non-well- composedness or weak connectivity of the document image. Next, a new digital similarity measure is introduced that outperforms the standard similarity measures, including the Hausdorff distance, with respect to determining if two discrete objects in the image are digitizations of the same prototype. This similarity measure is the minimum of four restricted Hausdorff distances. This measure is then used in a model-based compression algorithm. The compressed document image is not only much more compact than the original, but is also much closer to a actual monotonic digitization. Finally, we demonstrate that the same methods can be extended to finding structure in images taken under affine and perspective projection.

Paper Details

Date Published: 20 October 1997
PDF: 10 pages
Proc. SPIE 3168, Vision Geometry VI, (20 October 1997); doi: 10.1117/12.292785
Show Author Affiliations
Ari David Gross, CUNY/Queens College (United States)
Ruben Lusinyants, CUNY/Queens College (United States)

Published in SPIE Proceedings Vol. 3168:
Vision Geometry VI
Robert A. Melter; Angela Y. Wu; Longin Jan Latecki, Editor(s)

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