General Adaptive Neighborhood Image Processing: |
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Authors: | Johan Debayle Jean-Charles Pinoli |
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Affiliation: | 1. Ecole Nationale Supérieure des Mines de Saint-Etienne, Centre Ingénierie et Santé (CIS), Laboratoire LPMG, UMR CNRS, 5148, France
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Abstract: | The so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively
with its theoretical and practical aspects.
The Adaptive Neighborhood (AN) paradigm allows the building of new image processing transformations using context-dependent
analysis. Such operators are no longer spatially invariant, but vary over the whole image with ANs as adaptive operational
windows, taking intrinsically into account the local image features. This AN concept is here largely extended, using well-defined mathematical concepts,
to that General Adaptive Neighborhood (GAN) in two main ways. Firstly, an analyzing criterion is added within the definition of the ANs in order to consider the radiometric, morphological or geometrical characteristics
of the image, allowing a more significant spatial analysis to be addressed. Secondly, general linear image processing frameworks are introduced in the GAN approach, using concepts of abstract linear algebra, so as to develop operators that
are consistent with the physical and/or physiological settings of the image to be processed. |
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Keywords: | |
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