Abstract
The project described here was motivated by months of my sitting in front of an image processing system, trying different parameters and operations on a series of similar images, in the hope of developing one algorithm to work for all cases. (The problem was to try to locate the blood vessels in digital coronary angiograms). I remarked that I was spending an enormous amount of time computing partial results for each example image, testing the applicability of operations to subproblems, and looking for ideal parameter ranges over all of the examples considered together. It seemed to me that much of this work could be automated, simply by developing high level routines to do testing and parameter searches on a whole series of examples at once, rather than having me test each one ‘by hand’. Going further, it seemed that it should be possible to formalize the applicability of operations to a particular problem, and beyond that to formalize the entire search process for an algorithm. That is what this book is about, and it essentially arose out of my own experience and frustrations at performing these same steps manually for a period of two years while at Thomson-CGR in France, and subsequently at Machine Vision International (MVI) and at the Environmental Research Institute of Michigan (ERIM) in the United States.
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The situation has now begun to change with the work of Ritter & Wilson [1987], and Dougherty & Giardina [1987], both of which represent efforts to unify the field.
See Rich & Waters [1988] for a recent review of this field.
A genetic algorithm [Holland 1975].
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© 1989 Springer-Verlag New York Inc.
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Vogt, R.C. (1989). Introduction. In: Automatic Generation of Morphological Set Recognition Algorithms. Springer Series in Perception Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-9652-9_1
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DOI: https://doi.org/10.1007/978-1-4613-9652-9_1
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