Spatially and Intensity Adaptive Morphology

Autor: Johan Debayle, Jc 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), Institut Fédératif de Recherche en Sciences et Ingénierie de la Santé (IFRESIS-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-IFR143, Surfaces et Tissus Biologiques (STBio-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: 2012
Předmět:
Morphological gradient
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Adaptive morphology
Top-hat transform
Image processing
Context (language use)
02 engineering and technology
Mathematical morphology
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
connected operators
0202 electrical engineering
electronic engineering
information engineering
generalized linear image processing
Computer vision
general adaptive neighborhood image processing
[SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering
Electrical and Electronic Engineering
Image restoration
Mathematics
Feature detection (computer vision)
image filtering
business.industry
semi-flat morphology
020207 software engineering
Adaptive filter
Signal Processing
020201 artificial intelligence & image processing
Artificial intelligence
business
stack filtering
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Zdroj: IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing, IEEE, 2012, 6 (7), pp.820-829. ⟨10.1109/JSTSP.2012.2214762⟩
ISSN: 1932-4553
DOI: 10.1109/JSTSP.2012.2214762⟩
Popis: International audience; In this paper, spatially and intensity adaptive morphology is introduced and studied in the context of the General Adaptive Neighborhood Image Processing (GANIP) approach. The combination of GAN (General Adaptive Neighborhood)-based filtering and semi-flat morphology is particularly efficient in the sense that the filtering is adaptive to the image spatial structures (structuring elements are spatially variant) and its activity is controlled according to the image intensities (level sets are processed at different scales). The resulting morphological filters show a high image processing performance while preserving the image regions and details without damaging its transitions. The effectiveness of these adaptive operators are practically highlighted on real application examples for image background removing, image restoration and image enhancement.
Databáze: OpenAIRE
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