Abstract
In a series of eleven previous papers a radically different method of implementing a wide range of neighborhood image processing operations has been presented, under the acronym SKIPSM (separated-kernel image processing using finite state machines). Simply by changing the contents of two lookup tables, one can use the same software code or the same hardware configuration can carry out a long list of operations, including binary morphology with multiple large structuring elements, multiple simultaneous steps of the grassfire transform, 'fuzzy' binary morphological operations, grey-level morphology, binary skeletonization, binary correlation, binary openings and closings in one pass, and certain global image processing operations. The execution time is very fast, and is totally independent of the size of the neighborhood or of the number of simultaneous operations being performed. This paper gives a detailed description of the steps for creating lookup tables for binary morphology.
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