Abstract
In the context of image processing, a major role is played by the features and primitives that describe the data under examination and on which the processing operation is performed. Images acquired by different sensors, for different parameter values tunings, and multi-dimensional and multi-temporal data are becoming easily available, thus increasing the dimensionality of the classification space, then the need for feature-selection techniques.
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Dellepiane, S.G. (2000). The Importance of Features and Primitives for Multi-dimensional/Multi-channel Image Processing. In: Lisboa, P.J.G., Ifeachor, E.C., Szczepaniak, P.S. (eds) Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0487-2_20
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DOI: https://doi.org/10.1007/978-1-4471-0487-2_20
Publisher Name: Springer, London
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