This paper describes the application of SKIPSM (Separated-Kernel Image Processing using Finite State Machines) to binary morphology. In comparison with conventional hardware-based and software-based approaches, SKIPSM allow implementation at higher speeds and/or lower hardware cost. The key theoretical developments upon which this improved performance is based are the separation of 2D binary morphological image processing operations into a row operation followed by a column operation, the formulation of these row and column operations in a form compatible with pipelined operation, the implementation of the resulting operations as simple finite-state machine, and the automated generation of the finite-state machine configuration data. Some features of SKIPSM, as applied to binary morphology, are as follows: (1) The structuring elements (SEs) can be large (25 X 25 and larger) and arbitrary (with 'holes' and nonconvex shapes). (2) All types of morphology operations can be performed. (3) Multiple related or unrelated SEs can be applied simultaneously in a single pipeline pass. (4) Speed increases and/or neighborhood size increases by factors of 100 or more can be achieved. (5) Corresponding 'speedups' can be achieved in software-based implementations. (6) Inexpensive off-the-shelf 'chips' can be configured to carry out the same operations as expensive conventional hardware. (7) The user specifies the Se or set of simultaneous SEs. All other steps are automated. This paper includes some simple examples of the results and give implementation guidelines based on Se size and shape.

Date Published: 3 October 1994
PDF: 12 pages
Proc. SPIE 2347, Machine Vision Applications, Architectures, and Systems Integration III, (3 October 1994); doi: 10.1117/12.188750

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)