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General Adaptive Neighborhood Image Processing:

Part I: Introduction and Theoretical Aspects

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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.

In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting transforms perform a relevant spatially-adaptive image processing, in an intrinsic manner, that is to say without a priori knowledge needed about the image structures. Moreover, in several important and practical cases, the adaptive morphological operators are connected, which is an overwhelming advantage compared to the usual ones that fail to this property.

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Abbreviations

AN:

Adaptive Neighborhood

ANIP:

Adaptive Neighborhood Image Processing

ASE:

Adaptive Structuring Element

ASF:

Alternating Sequential Filter

CLIP:

Classical Linear Image Processing

IP:

Image Processing

GAN:

General Adaptive Neighborhood

GANIP:

General Adaptive Neighborhood Image Processing

GANMM:

General Adaptive Neighborhood Mathematical Morphology

GLIP:

General Linear Image Processing

LIP:

Logarithmic Image Processing

LRIP:

Log-Ratio Image Processing

MHIP:

Multiplicative Homomorphic Image Processing

MM:

Mathematical Morphology

SE:

Structuring Element.

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  1. Ecole Nationale Supérieure des Mines de Saint-Etienne, Centre Ingénierie et Santé (CIS), Laboratoire LPMG, UMR CNRS, 5148, France

    Johan Debayle & Jean-Charles Pinoli

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  1. Johan Debayle
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Johan Debayle received his Ph.D. degree in Image, Vision and Signal from the French graduate school ‘Ecole Nationale Supérieure des Mines de Saint-Etienne’ and the University of Saint-Etienne, France, in 2005. He is currently a postdoctoral scientist at the French National Institute for Research in Computer Science and Control (INRIA). His research interests include adaptive image processing and analysis.

Jean-Charles Pinoli received Master’s, Ph.D. and D. Sc. (Habilitation à Diriger des Recherches) degrees in Applied Mathematics in 1983, 1985 and 1992, respectively. From 1985 to 1989, he was member of the opto-electronics department of the Angenieux (Thales) company, Saint-Héand, France, where he pioneered researches in the field of digital imaging and artificial vision. In 1990, he joined the Corporate Research Center of the Pechiney Company, Voreppe, France and was member of the computational technologies department in charge of the imaging activities. Since 2001, he is full professor at the French graduate school ‘Ecole Nationale Supérieure des Mines de Saint-Étienne’. He leads the Image Processing and Pattern Analysis Group within the Engineering and Health research Center and the LPMG Laboratory, UMR CNRS 5148. His research interests and teaching include Image Processing, Image Analysis, Mathematical Morphology and Computer Vision.

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Debayle, J., Pinoli, JC. General Adaptive Neighborhood Image Processing:. J Math Imaging Vis 25, 245–266 (2006). https://doi.org/10.1007/s10851-006-7451-8

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