General Adaptive Neighborhood Image Processing. Part I: Introduction and Theoretical Aspects

Autor: Jean-Charles Pinoli, Johan Debayle
Přispěvatelé: Centre Ingénierie et Santé (CIS-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département Imagerie et Statistiques (DIS-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CIS, Laboratoire des Procédés en Milieux Granulaires (LPMG-EMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2006
Předmět:
Statistics and Probability
Theoretical computer science
Top-hat transform
Image processing
02 engineering and technology
Mathematical morphology
Image Processing Frameworks
Structuring
General Adaptive Neighborhoods
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
0202 electrical engineering
electronic engineering
information engineering
Invariant (mathematics)
Intrinsic Spatially-Adaptive Analysis
Mathematics
Applied Mathematics
020206 networking & telecommunications
Neighborhood operation
Condensed Matter Physics
Modeling and Simulation
Linear algebra
A priori and a posteriori
Nonlinear Image Representation
020201 artificial intelligence & image processing
Geometry and Topology
Computer Vision and Pattern Recognition
Mathematical Morphology
Algorithm
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Zdroj: Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision, Springer Verlag, 2006, 25(2), pp.245-266. ⟨10.1007/s10851-006-7451-8⟩
ISSN: 0924-9907
1573-7683
DOI: 10.1007/s10851-006-7451-8⟩
Popis: 30 pages; International audience; 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.
Databáze: OpenAIRE
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