EMSE

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: EMSE
Outline:
  • Context, Introduction, State-of-the art
  • GAN definition and properties
  • GAN Mathematical Morphology
  • GAN Choquet Filtering
  • GAN Pretopology
  • GAN Generalized Distances
  • GAN Integral Geometry
  • GAN Shape Diagrams
  • GAN Color
  • GAN Registration
  • Conclusion and Prospects
Main publications:
  1. J. Debayle and B. Presles. Rigid Image Registration by General Adaptive Neighborhood Matching. Pattern Recognition, 55:45-57, 2016.
  2. Y. Gavet, J. Debayle, and J. C. Pinoli. Color Image and Video Enhancement, chapter The Color Logarithmic Image Processing (CoLIP) antagonist space with application to image enhancement, pages 1-28. Springer, 2015.
  3. V. Gonzalez-Castro, J. Debayle, Y. Wazaefi, M. Rahim, C. Gaudy, J. J. Grob, and B. Fertil. Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions. Journal of Electronic Imaging, 24(6):061104/1-9, 2015.
  4. V. Gonzalez-Castro, J. Debayle, Y. Wazaefi, M. Rahim, C. Gaudy, J. J. Grob, and B. Fertil. Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns. In IEEE International Conference on Image Processing (ICIP), Québec, Canada, September 2015.
  5. J. Debayle and J. C. Pinoli. Advances in Low-Level Color Image Processing, chapter Spatially Adaptive Color Image Processing, pages 195-222. Springer, 2014.
  6. V. Gonzalez-Castro, J. Debayle, and J. C. Pinoli. Color adaptive neighborhood mathematical morphology and its application to pixel-level classification. Pattern Recognition Letters, 47(1):50-62, 2014.
  7. S. Rivollier, J. Debayle, and J. C. Pinoli. Adaptive shape diagrams for multiscale morphometrical image analysis. Journal of Mathematical Imaging and Vision, 49(1):51-68, 2014.
  8. V. Gonzalez-Castro, J. Debayle, and V. Curic. Pixel Classification using General Adaptive Neighborhood-based Features. In Proceedings of the 22nd International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, 2014.
  9. B. Presles, J. Debayle, and J. C. Pinoli. A novel projective stereological image analysis method to estimate particle size and shape distributions. In 10th IEEE/SPIE International Conference on Quality Control by Artificial Vision (QCAV), Fukuoka, Japan, May-June 2013.
  10. J. C. Pinoli and J. Debayle. Adaptive Generalized Metrics, Distance Maps and Nearest Neighbor Transforms on Gray Tone Images. Pattern Recognition, 45:2758-2768, 2012.
  11. J. C. Pinoli and J. Debayle. Spatially and intensity adaptive morphology. IEEE Journal of Selected Topics in Signal Processing, 6(7):820-829, 2012.
  12. B. Presles, J. Debayle, and J. C. Pinoli. Shape recognition from shadows of 3-D convex geometrical objects. In IEEE International Conference on Image Processing (ICIP), Orlando, USA, September/October 2012.
  13. J. Debayle and J. C. Pinoli. Advances in Imaging and Electron Physics, volume 167, chapter Theory and Applications of General Adaptive Neighborhood Image Processing, pages 121-183. Elsevier, 2011.
  14. J. Debayle and J. C. Pinoli. General Adaptive Neighborhood-based Pretopological Image Filtering. Journal of Mathematical Imaging and Vision, 41(3):210-221, 2011.
  15. J. Debayle and J. C. Pinoli. General adaptive neighborhood viscous mathematical morphology. In 10th International Symposium on Mathematical Morphology (ISMM), Lecture Notes in Computer Science, Intra, Italy, July 2011.
  16. J. C. Pinoli and J. Debayle. General adaptive distance transforms on gray-tone images: application to image segmentation. In IEEE International Conference on Image Processing (ICIP), Brussell, Belgium, September 2011.
  17. S. Rivollier, J. Debayle, and J. C. Pinoli. Integral Geometry and General Adaptive Neighborhood for Multiscale Image Analysis. International Journal of Signal and Image Processing, 1(3):141-150, 2010.
  18. B. Presles, J. Debayle, G. Cameirao, A. a,d Fevotte, and J. C. Pinoli. Volume estimation of 3D particles with known convex shapes from its projected areas. In IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2010.
  19. S. Rivollier, J. Debayle, and J. C. Pinoli. Shape representation and analysis of 2D compact sets by shape diagrams. In IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2010.
  20. J. Debayle and J. C. Pinoli. General Adaptive Neighborhood Choquet Image Filtering. Journal of Mathematical Imaging and Vision, 35(3):173-185, November 2009.
  21. J. C. Pinoli and J. Debayle. General Adaptive Neighborhood Mathematical Morphology. In IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009.
  22. J. Debayle and J. C. Pinoli. General Adaptive Neighborhood Representation for Adaptive Choquet Image Filtering. In 10th European Congress of Stereology and Image Analysis (ECSIA), Milan, Italy, pages 431-436, June 2009.
  23. B. Presles, J. Debayle, A. Rivoire, G. Févotte, and J. C. Pinoli. Monitoring the particle size distribution using image analysis during batch crystallization processes. In 9th IEEE/SPIE International Conference on Quality Control by Artificial Vision (QCAV), Wels, Austria, May 2009.
  24. S. Rivollier, J. Debayle, and J. C. Pinoli. General Adaptive Neighborhood-Based Minkowski Maps for Gray-Tone Image Analysis. In 10th European Congress of Stereology and Image Analysis (ECSIA), Milan, Italy, pages 219-224, June 2009.
  25. J. C. Pinoli and J. Debayle. Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception and Application Issues. EURASIP Journal on Advances in Signal Processing, 2007(Article ID 36105):1-22, 2007.
  26. J. Debayle and J. C. Pinoli. General Adaptive Neighborhood Image Processing - Part I: Introduction and Theoretical Aspects. Journal of Mathematical Imaging and Vision, 25(2):245-266, September 2006.
  27. J. Debayle and J. C. Pinoli. General Adaptive Neighborhood Image Processing - Part II: Practical Application Examples. Journal of Mathematical Imaging and Vision, 25(2):267-284, September 2006.

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