Abstract
One of the difficulties that has been apparent in applying image processing and understanding algorithms is that of the optimal choice of parameters and the algorithms themselves. Firstly we must select an algorithm and secondly the actual parameters that are required by that algorithm. It is also the case that using a chosen algorithm on a different image class yields results of a totally different quality, we have considered three image classes, namely infra-red linescan, Russian satellite and SPOT imagery. We have explored the use of genetic algorithms for the purpose of parameter and algorithm selection and will show how the approach can successfully obtain results which in the past have tended to be obtained somewhat heuristically. Once a reliable region has been obtained then we can represent its shape using a curvature scale space description.The main application of this work will be in the area of image databases.
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© 1998 Springer-Verlag London Limited
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Ducksbury, P.G. (1998). Image Segmentation & the use of Genetic Algorithms for Optimising Parameters and Algorithm choice. In: Marshall, S., Harvey, N.R., Shah, D. (eds) Noblesse Workshop on Non-Linear Model Based Image Analysis. Springer, London. https://doi.org/10.1007/978-1-4471-1597-7_22
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DOI: https://doi.org/10.1007/978-1-4471-1597-7_22
Publisher Name: Springer, London
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