General Adaptive Neighborhood Image Processing. Part II: Practical Applications Issues

Autor: Johan Debayle, Jean-Charles Pinoli
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
Property (programming)
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Context (language use)
02 engineering and technology
Mathematical morphology
Image Processing Frameworks
Structuring
Image (mathematics)
General Adaptive Neighborhoods
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
0202 electrical engineering
electronic engineering
information engineering
Intrinsic Spatially-Adaptive Analysis
Applied Mathematics
Contrast (statistics)
020206 networking & telecommunications
Image segmentation
Condensed Matter Physics
Modeling and Simulation
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.267-284. ⟨10.1007/s10851-006-7452-7⟩
ISSN: 0924-9907
1573-7683
DOI: 10.1007/s10851-006-7452-7⟩
Popis: 23 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 General Adaptive Neighborhood (GAN) paradigm, theoretically introduced in Part I [20], allows the building of new image processing transformations using context-dependent analysis. With the help of a specified analyzing criterion, such transformations perform a more significant spatial analysis, taking intrinsically into account the local radiometric, morphological or geometrical characteristics of the image. Moreover they are consistent with the physical and/or physiological settings of the image to be processed, using general linear image processing frameworks. 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 morphological operators perform a really spatiallyadaptive image processing and notably, in several important and practical cases, are connected, which is a great advantage compared to the usual ones that fail to this property. Several GANIP-based results are here exposed and discussed in image filtering, image segmentation, and image enhancement. In order to evaluate the proposed approach, a comparative study is as far as possible proposed between the adaptive and usual morphological operators. Moreover, the interests to work with the Logarithmic Image Processing framework and with the 'contrast' criterion are shown through practical application examples.
Databáze: OpenAIRE
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