Abstract
The application of a mathematical operation to each neighbourhood in an image is called convolution. The operation is defined by a “mask” specifying for each neighbourhood, how many points it contains and how the corresponding image point affects the computations. Each location in the operator mask contains a weighting value, these are multiplied by the value of the corresponding image location and the results summed to give the convolution value for that neighbourhood. Doing this for all neighbourhoods produces a new array of values. Mathematically, the convolution integral is the integrated cross product of a weighting function with an image. See local grey-level operations <127>.
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Reference
Frisby, J. P. Seeing. Oxford University Press, 1979.
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© 1984 Springer-Verlag Berlin Heidelberg
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Bundy, A., Wallen, L. (1984). Convolution. In: Bundy, A., Wallen, L. (eds) Catalogue of Artificial Intelligence Tools. Symbolic Computation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-96868-6_45
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DOI: https://doi.org/10.1007/978-3-642-96868-6_45
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