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Proceedings Paper

Minimal-memory bit vector architecture for computational mathematical morphology
Author(s): John C. Handley
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Paper Abstract

Computational mathematical morphology (CMM) is a nonlinear filter representation particularly amenable to real-time image processing. In the state of the art implementation each pixel value in a windowed observation is indexed into a separate lookup table to retrieve a set of bit vectors. Each bit in the vector corresponds to a basis element in the CMM filter representation. All retrieved bit vectors are "anded" together to produce a bit vector with a unique nonzero bit. The position of that bit corresponds to a basis element containing the observation and it used to look up a filter value in a table. The number of stored bit vectors is a linear function of the image or signal bit depth. We present an architecture for CMM implementation that uses a minimal number of bit vectors and required memory is less sensitive to bit depth. In the proposed architecture, basis elements are projected to subspaces and only bit vectors unique to each subspace are stored. With the addition of an intermediate lookup table to map observations to unique bit vectors, filter memory is greatly reduced. Simulations show that the architecture provides an advantage for random tessellations of the observation space. A 50% memory savings is shown for a practical application to digital darkness control in electronic printing.

Paper Details

Date Published: 28 May 2004
PDF: 9 pages
Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); doi: 10.1117/12.525712
Show Author Affiliations
John C. Handley, Xerox Corp. (United States)


Published in SPIE Proceedings Vol. 5298:
Image Processing: Algorithms and Systems III
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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