Edge Detection of Petrographic Images Using Genetic Programming

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Brian J. Ross, F. Fueten, and D.Y. Yashkir: Edge Detection of Petrographic Images Using Genetic Programming. GECCO 2000, ed. D. Whitley et al., Morgan Kaufmann, 2000, pp. 658-665



This paper discusses work in progress that uses genetic programming to evolve edge de- tectors for petrographic images. Microscopic images of thin sections from mineral samples are obtained using a rotating polarizer mi- croscope. These images are then processed using a number of lters, resulting in a set of nine ltered image parameters. In order to be useful for higher{level analysis, such as automatic mineral identi cation, the grain boundaries within these images must be iden- ti ed. Using genetic programming, edge de- tecting functions are evolved for this purpose. The edge detectors may use as any of the l- tered image parameters as input. Since the source images are large, a subset of the im- ages is sampled for training, and the remain- der of the image is used for testing. This training data is selected with a biased ran- dom sampling strategy. The complexity of the images dictates that a generic edge de- tector for all mineral specimens is infeasible. Rather, the most useful edge detectors will be those that are specialized for particular families of mineral specimens.

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