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Proceedings Paper

Partitioning schemes for use in a neural network for digital image halftoning
Author(s): Jean R. S. Blair; Tommy D. Wagner; David A. Nash; Eugene K. Ressler; Barry L. Shoop; Timothy J. Talty
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Paper Abstract

In this research, we investigate partitioning schemes for reducing the computational complexity of an error diffusion neural network (EDN) for the application of digital halftoning. We show that by partitioning the original image into k subimages, the time required to perform the halftoning using an EDN is reduced by as much as a factor of k. Motivated by this potential speedup, we introduce three approaches to partitioning with varying degrees of overlap and communication between the partitions. We quantitatively demonstrate that the Constrained Framing approach produces halftoned images whose quality is as good as the quality of halftoned images produced by the EDN without partitioning.

Paper Details

Date Published: 30 March 2000
PDF: 13 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380594
Show Author Affiliations
Jean R. S. Blair, U.S. Military Academy (United States)
Tommy D. Wagner, U.S. Military Academy (United States)
David A. Nash, U.S. Military Academy (United States)
Eugene K. Ressler, U.S. Military Academy (United States)
Barry L. Shoop, U.S. Military Academy (United States)
Timothy J. Talty, U.S. Military Academy (United States)

Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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