Abstract
The implementation of binary morphological image processing operators using artificial neural networks (ANN)s is addressed. The emphasis is on network and unit architectures and training algorithms for Dilation, Erosion, Opening and Closing. Other concerns include training set design, training of local operators with and without specifying a local interConnectivity (receptive field), generalization, linear separability of training sets, and learning algorithms for (multi-) layer threshold units.
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© 1996 Kluwer Academic Publishers
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Herwig, C.B., Schalkoff, R.J. (1996). Implementing Morphological Image Operators Via Trained Neural Networks. In: Maragos, P., Schafer, R.W., Butt, M.A. (eds) Mathematical Morphology and its Applications to Image and Signal Processing. Computational Imaging and Vision, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0469-2_28
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DOI: https://doi.org/10.1007/978-1-4613-0469-2_28
Publisher Name: Springer, Boston, MA
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