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Proceedings Paper

Separated-kernel image processing using finite-state machines (SKIPSM)
Author(s): Frederick M. Waltz
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Paper Abstract

This paper presents a fundamentally new way to carry out many standard image processing operations. In comparison with conventional hardware-based and software-based approaches, SKIPSM (Separated-Kernel Image Processing using Finite State Machines) allows implementation at higher speeds and/or lower hardware cost. The key features of SKIPSM are (1) the separation of a large class of neighborhood image processing operations (generally considered not to be separable) into a row operation followed by a column operation, (2) the formulation of these row and column operations in a form compatible with pipelined operations, (3) the implementation of the resulting operations as simple finite-state machines, and (4) the automated generation of the finite-state machine configuration data. Speed increases and/or neighborhood size increased by factors of 100 or more are achieved using conventional pipelined hardware in this new way. Alternatively, inexpensive off-the-shelf 'chips' can be configured to carry out the same operations as conventional hardware. Corresponding 'speedups' are achieved in software- based implementations. Furthermore, it is often possible to use SKIPSM to carry out 10 of more different image processing operations simultaneously, with no additional processing steps or hardware.

Paper Details

Date Published: 3 October 1994
PDF: 10 pages
Proc. SPIE 2347, Machine Vision Applications, Architectures, and Systems Integration III, (3 October 1994); doi: 10.1117/12.188749
Show Author Affiliations
Frederick M. Waltz, Univ. of Minnesota (United States)

Published in SPIE Proceedings Vol. 2347:
Machine Vision Applications, Architectures, and Systems Integration III
Bruce G. Batchelor; Susan Snell Solomon; Frederick M. Waltz, Editor(s)

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