TUTORIAL @ICPR 2022
General Adaptive Neighborhood Image Processing and Analysis (GANIPA)
General Information
Duration: | Half-Day (3 hours) |
Location: | On-Site |
Audience: | Academia including graduate students, practicing engineers, researchers and professors |
Background: | Applied mathematics, image processing and analysis |
Tutorial Description
Keywords: | Local image representation, image filtering and segmentation, shape analysis, object detection, image classification, image registration |
Abstract: |
The framework entitled General Adaptive Neighborhood Image Processing and Analysis (GANIPA) has been introduced in order to propose an original local image representation and mathematical structure for adaptive non-linear processing and analysis of gray-tone images and further extended to color and multispectral images. The central idea is based on the key notion of adaptivity which is simultaneously associated with the analyzing scales, the spatial structures and the intensity values of the image to be addressed. Several adaptive image operators are then defined in the context of image filtering, image segmentation, image measurements and image registration by the use of convolution analysis, order filtering, mathematical morphology, integral geometrical or similarity measures. Such operators are no longer spatially invariant, but vary over the whole image with General Adaptive Neighborhoods (GANs) as adaptive operational windows, taking intrinsically into account the local image features.
The first part of this tutorial will be focused on the context and the definitions and properties of the GANs. Once these adaptive neighborhoods are defined, it is possible to build different operators for image processing (filtering such as enhancement/restoration, segmentation, registration...) but also for image analysis providing tools for local image measurements (for shape analysis, object detection, image classification). The second part of my talk will be focused on these new operators and will be illustrated on real applications in different areas (biomedical, material, process engineering, remote sensing...). Finally, some conclusions and prospects will be given. In conclusion, the GANIPA framework allows efficient adaptive image operators to be built (using local adaptive operational windows) and opens new pathways that promise large prospects for image and pattern analysis. |
Illustrations: | |
Outline: |
|
Main publications: |
|
Presenter
Name: |
Prof. Johan DEBAYLE IET Fellow, IACSIT Fellow, IEEE Senior Member |
Affiliation: | MINES Saint-Etienne, France |
Short Biography: | Johan Debayle received his M.Sc., Ph.D. and Habilitation degrees in the field of image processing and analysis, in 2002, 2005 and 2012 respectively. Currently, he is a Full Professor at the Ecole Nationale Supérieure des Mines de Saint-Etienne (MINES Saint-Etienne) in France, within the SPIN Center and the LGF Laboratory, UMR CNRS 5307, where he leads both the PMMG and PMDM departments, respectively, which are interested in image processing and analysis of granular media. He is also the Deputy Director of the MORPHEA CNRS GDR 2021 Research Group. He is the General Chair/Co-Chair of several international conferences (IEEE ISIVC'2020, ECSIA'2021, ICIVP'2021, ICMV'2021, ISIVC'2022, ICPRS'2022). He is Associate Editor of 6 Int. Journals (PRL, PAA, JEI, IAS, JoI, IET-IP). His research interests include image processing and analysis, mathematical morphology, pattern recognition and stochastic geometry. He published more than 150 international papers in book chapters, international journals and conference proceedings. He has been Invited Professor in different universities: ITWM Fraunhofer / University of Kaisersleutern (Germany), University Gadjah Mada, Yogyakarta (Indonesia), University of Puebla (Mexico) and is currently co-supervisor of several PhD students with these partners. He served as Program committee member in several international conferences (ICPRS, IEEE ICIP, ICMV...). He has been invited as keynote speaker in several international conferences (ICPRS, SPIE EI, ICMV…). He is a reviewer for several international journals (PR, PRL, PAA, IEEE-TIP, JMIV...). He is Fellow of IET, Fellow of IACSIT, Senior Member of IEEE, Member of the IAPR, and member of the board of directors of AFRIF (IAPR France Section). |
Contact: | debayle@emse.fr |
Homepage: | https://www.emse.fr/~debayle/index.html |