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Proceedings Paper

Pattern recognition and image reconstruction using improved digital Zernike moments
Author(s): Huibao Lin; Jennie Si; Glen P. Abousleman
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Paper Abstract

Zernike moments are one of the most effective orthogonal, rotation-invariant moments in continuous space. Unfortunately, the digitization process necessary for use with digital imagery results in compromised orthogonality. In this work, we introduce improved digital Zernike moments that exhibit much better orthogonality, while preserving their inherent invariance to rotation. We then propose a novel pattern recognition algorithm that is based on the improved digital Zernike moments. With the improved orthogonality, targets can be represented by fewer moments, thus minimizing computational complexity. Additionally, the rotation invariance enables our algorithm to recognize targets with arbitrary orientation. Because our algorithm eliminates the segmentation step that is typically applied in other techniques, it is better suited to low-quality imagery. Simulations on real images demonstrate these aspects of the proposed algorithm.

Paper Details

Date Published: 28 March 2005
PDF: 10 pages
Proc. SPIE 5816, Optical Pattern Recognition XVI, (28 March 2005); doi: 10.1117/12.604076
Show Author Affiliations
Huibao Lin, Arizona State Univ. (United States)
Jennie Si, Arizona State Univ. (United States)
Glen P. Abousleman, General Dynamics C4 Systems (United States)

Published in SPIE Proceedings Vol. 5816:
Optical Pattern Recognition XVI
David P. Casasent; Tien-Hsin Chao, Editor(s)

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