Abstract
A new approach for the application of genetic programming to vision problems is presented. Sets of atomic subprograms are genetically combined to solve more advanced problems within low-level vision or image preprocessing. We present the main ideas and give a brief sketch of their implementation in the distributed simulation environment GENCODER. This system forms the basis for some introductory experiments obtained. Finally, some aspects of the gained results together with interesting possibilities for future research are portrayed.
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References
J. Aloimonos and D. Shulman. ‘Integration of Visual Modules — An Extension of the Marr Paradigm’. Academic Press, 1989.
J. Koza. ‘Genetic Programming — On the Programming of Computers by Means of Natural Selection’. MIT Press, 1992.
T. Poggio, V. Torre, and C. Koch. ‘Computational vision and regularization theory’. Nature 317, 1985.
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© 1998 Springer-Verlag Wien
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Ziemeck, P., Ritter, H. (1998). Evolving Low-Level Vision Capabilities with the GENCODER Genetic Programming Environment. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_17
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_17
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive