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Proceedings Paper

High-performance thresholding with adaptive equalization
Author(s): Ka Po Lam
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Paper Abstract

The ability to simplify an image whilst retaining such crucial information as shapes and geometric structures is of great importance for real-time image analysis applications. Here the technique of binary thresholding which reduces the image complexity has generally been regarded as one of the most valuable methods, primarily owing to its ease of design and analysis. This paper studies the state of developments in the field, and describes a radically different approach of adaptive thresholding. The latter employs the analytical technique of histogram normalization for facilitating an optimal `contrast level' of the image under consideration. A suitable criterion is also developed to determine the applicability of the adaptive processing procedure. In terms of performance and computational complexity, the proposed algorithm compares favorably to five established image thresholding methods selected for this study. Experimental results have shown that the new algorithm outperforms these methods in terms of a number of important errors measures, including a consistently low visual classification error performance. The simplicity of design of the algorithm also lends itself to efficient parallel implementations.

Paper Details

Date Published: 21 September 1998
PDF: 10 pages
Proc. SPIE 3452, Parallel and Distributed Methods for Image Processing II, (21 September 1998); doi: 10.1117/12.323466
Show Author Affiliations
Ka Po Lam, Univ. of Keele (United Kingdom)

Published in SPIE Proceedings Vol. 3452:
Parallel and Distributed Methods for Image Processing II
Hongchi Shi; Patrick C. Coffield, Editor(s)

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