Skip to main content
SpringerLink
Log in

General Adaptive Neighborhood Image Processing:

Part I: Introduction and Theoretical Aspects

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

AN:

Adaptive Neighborhood

ANIP:

Adaptive Neighborhood Image Processing

ASE:

Adaptive Structuring Element

ASF:

Alternating Sequential Filter

CLIP:

Classical Linear Image Processing

IP:

Image Processing

GAN:

General Adaptive Neighborhood

GANIP:

General Adaptive Neighborhood Image Processing

GANMM:

General Adaptive Neighborhood Mathematical Morphology

GLIP:

General Linear Image Processing

LIP:

Logarithmic Image Processing

LRIP:

Log-Ratio Image Processing

MHIP:

Multiplicative Homomorphic Image Processing

MM:

Mathematical Morphology

SE:

Structuring Element.

References

  1. L. Alvarez, F. Guichard, P.L. Lions, and J.M. Morel, “Axioms and fundamental equations in image processing,” Arch. for Rational Mechanics, Vol. 123, pp. 199–257, 1993.

    Article  MATH  MathSciNet  Google Scholar 

  2. G.R. Arce and R.E. Foster, “Detail-preserving ranked-order based filters for image processing,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 37, No. 1, pp. 83–98, 1989.

    Article  Google Scholar 

  3. J. Astola and P. Kuosmanen, Fundamentals of Nonlinear Digital Filtering, CRC Press, Boca Raton, New York, USA, 1997.

    Google Scholar 

  4. S. Beucher and C. Lantuejoul, “Use of watersheds in contour detection,” in International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, Rennes, France, 1979.

  5. S. Beucher and F. Meyer, “The morphological approcah to segmentation: The watershed transformation,” in Mathematical Morphology in Image Processing, E. Dougherty (Ed.), N.Y., USA, pp. 433–481, 1993.

  6. U.d.M. Braga Neto, “Alternating sequential filters by adaptive-neighborhood structuring functions,” in, P. Maragos, R.W. Schafer, and M.A. Butt (Eds.), Mathematical Morphology and its Applications to Image and Signal Processing, pp. 139–146, 1996.

  7. J.C. Brailean, B.J. Sullivan, C.T. Chen, and M.L. Giger, “Evaluating the EM algorithm for image processing using a human visual fidelity criterion,” in Proceedings of the International Conference on Acoustics, Speech and Signal Processing, pp. 2957–2960, 1991.

  8. V. Buzuloiu, M. Ciuc, R.M. Rangayyan, and C. Vertan, “Adaptive-neighborhood histogram equalization of color images,” Electronic Imaging, Vol. 10, No. 2, pp. 445–459, 2001.

    Article  Google Scholar 

  9. E. Cech, Topological Spaces, John Wiley & Sons Ltd., Prague, Czechoslovakia, 1966.

    MATH  Google Scholar 

  10. M. Charif-Chefchaouni and D. Schonfeld, “Spatially-variant mathematical morphology,” in Proceedings of the IEEE International Conference on Image Processing, Austin, Texas, USA, pp. 13–16, November 1994.

  11. J.C. Chazallon and L. Pinoli, “An automatic morphological method for aluminium grain segmentation in complex grey level images,” Acta Stereologica, Vol. 16, No. 2, pp. 119–130, 1997.

    Google Scholar 

  12. G. Choquet, Topology, Academic Press, New-York, USA, 1966.

    MATH  Google Scholar 

  13. G. Choquet, Cours De Topologie, chapt. Espaces topologiques et espaces métriques, Dunod: Paris, France, 2000, pp. 45–51.

  14. M. Ciuc, “Traitement d’images multicomposantes: Application à l’imagerie couleur et radar,” Ph.D. thesis, Université de Savoie - Université Polytechnique de Bucarest, Roumanie, 2002.

  15. M. Ciuc, R.M. Rangayyan, T. Zaharia, and V. Buzuloiu, “Filtering noise in color images using adaptive-neighborhood statistics,” Electronic Imaging, Vol. 9, No. 4, pp. 484–494, 2000.

    Article  Google Scholar 

  16. J. Crespo, J. Serra, and R.W. Schafer, “Theoretical aspects of morphological filters by reconstruction,” Signal Processing, Vol. 47, pp. 201–225, 1995.

    Article  Google Scholar 

  17. O. Cuisenaire, “Locally adaptable mathematical morphology,” in Proceedings of the IEEE International Conference on Image Processing, Genova, Italy, pp. 11–14, September 2005.

  18. S.C. Dakin and R.F. Hess, “The spatial mechanisms mediating symmetry perception,” Vision Research, Vol. 37, pp. 2915–2930, 1997.

    Article  Google Scholar 

  19. J. Debayle, “General adaptive neighborhood image processing,” Ph.D. thesis, Ecole Nationale Supérieure des Mines, Saint-Etienne, France, 2005.

  20. J. Debayle and J.C. Pinoli, “General adaptive neighborhood image processing—Part I: Introduction and theoretical aspects,” Journal of Mathematical Imaging and Vision, submitted paper.

  21. J. Debayle and J.C. Pinoli, “General adaptive neighborhood image processing—Part II: practical application examples,” Journal of Mathematical Imaging and Vision, submitted paper.

  22. J. Debayle and J.C. Pinoli, “Adaptive-neighborhood mathematical morphology and its applications to image filtering and segmentation,” in 9th European Congress on Stereology and Image Analysis, Zakopane, Poland, pp. 123–130, 2005a.

  23. J. Debayle and J.C. Pinoli, “Multiscale image filtering and segmentation by means of adaptive neighborhood mathematical morphology,” in Proceedings of the IEEE International Conference on Image Processing, Genova, Italy, pp. 537–540, 2005b.

  24. J. Debayle and J.C. Pinoli, “Spatially adaptive morphological image filtering using intrinsic structuring elements,” Image Analysis and Stereology, Vol. 24, No. 3, pp. 145–158, 2005c.

    MathSciNet  MATH  Google Scholar 

  25. G. Deng and L.W. Cahill, “Multiscale image enhancement using the logarithmic image processing model,” Electronic Letters, Vol. 29, pp. 803–804, 1993.

    Google Scholar 

  26. G. Deng, L.W. Cahill, and J.R. Tobin, “A study of the logarithmic image processing model and its application to image enhancement,” IEEE Transactions on Image Processing, Vol. 4, pp. 506–512, 1995.

    Article  Google Scholar 

  27. G. Deng and J.C. Pinoli, “Differentiation-based detection using the logarithmic image processing model,” Journal of Mathematical Imaging and Vision, Vol. 8, pp. 161–180, 1998.

    Article  MathSciNet  Google Scholar 

  28. G. Deng, J.C. Pinoli, W.Y. Ng, L.W. Cahill, and M. Jourlin, “A comparative study of the Log-ratio image processing approach and the logarithmic image processing model,” unpublished manuscript, 1994.

  29. E.R. Dougherty and J. Astola, An Introduction to Nonlinear Image Processing, SPIE Press, Bellingham, 1994.

    Google Scholar 

  30. N. Dunford and J.T. Schwartz, Linear Operators, Part I, General Theory, Wiley-Interscience, New-York, USA, 1988.

    MATH  Google Scholar 

  31. R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley, 1992.

  32. R. Gordon and R.M. Rangayyan, “Feature enhancement of mammograms using fixed and adaptive neighborhoods,” Applied Optics, Vol. 23, No. 4, pp. 560–564, 1984.

    Article  Google Scholar 

  33. D.J. Granrath, “The role of human visual models in image processing,” in Proceedings of the IEEE, pp. 552–561, 1981.

  34. P. Gremillet, M. Jourlin, and J.C. Pinoli, “LIP model-based three-dimensionnal reconstruction and visualisation of HIV infected entire cells,” Journal of Microscopy, Vol. 174, pp. 31–38, 1994.

    Google Scholar 

  35. M. Grimaud, “La géodésie numérique en morphologie mathématique. Application à la détection automatique de microcalcifications en mammographie numérique,” Ph.D. thesis, Ecole Nationale Supérieure des Mines de Paris, France, 1991.

  36. J.E. Hafstrom, Introduction to Analysis and Abstract Algebra, W.B. Saunders: Philadelphia, USA, 1967.

    MATH  Google Scholar 

  37. P. Hawkes, “Image algebra and rank-order filters,” Scanning Microscopy, Vol. 11, pp. 479–482, 1997.

    Google Scholar 

  38. H.J.A.M. Heijmans and R.V.D. Boomgaard, “Algebraic framework for linear and morphological scale-spaces,” Journal of Visual Communication and Image Representation, Vol. 13, Nos. 1/2, pp. 269–301, 2000.

    Google Scholar 

  39. A.K. Jain, “Advances in mathematical models for image processing,” in Proceedings of the IEEE, pp. 502–528, 1981.

  40. K. Jänich, Topology, Springer, Berlin, Germany, 1983.

    Google Scholar 

  41. M. Jourlin and J.C. Pinoli, “Logarithmic image processing,” Acta Stereologica, Vol. 6, pp. 651–656, 1987.

    Google Scholar 

  42. M. Jourlin and J.C. Pinoli, “A model for logarithmic image processing,” Journal of Microscopy, Vol. 149, pp. 21–35, 1988.

    Google Scholar 

  43. M. Jourlin and J.C. Pinoli, “Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model,” Signal Processing, Vol. 41, pp. 225–237, 1995.

    Article  MATH  Google Scholar 

  44. M. Jourlin and J.C. Pinoli, “Logarithmic image processing: The mathematical and physical framework for the representation and processing of transmitted images,” Advances in Imaging and Electron Physics, Vol. 115, pp. 129–196, 2001.

    Google Scholar 

  45. M. Jourlin, J.C. Pinoli, and R. Zeboudj, “Contrast definition and contour detection for logarithmic images,” Journal of Microscopy, Vol. 156, pp. 33–40, 1988.

    Google Scholar 

  46. L. Kantorovitch and G. Akilov, Analyse Fonctionnelle, Editions Mir, Moscou, Russia, 1981.

    Google Scholar 

  47. J.L. Kelley, General Topology, D. Van Nostrand, New-York, USA, 1955.

    MATH  Google Scholar 

  48. S. Lang, Linear Algebra, Addison Wesley, Reading, MA, 1966.

    MATH  Google Scholar 

  49. G. Laporterie, F. Flouzat, and O. Amram, “The morphological pyramid and its applications to remote sensing: Multiresolution data analysis and features extraction,” Image Analysis and Stereology, Vol. 21, No. 1, pp. 49–53, 2002.

    Google Scholar 

  50. J.S. Lee, “Refined filtering of image using local statistics,” Computer Graphics and Image Processing, Vol. 15, pp. 380–389, 1981.

    Google Scholar 

  51. R Lerallut, E Decencire, and F. Meyer, “Image filtering using morphological amoebas,” in C. Ronse, L. Najman, and E. Decencire (Eds.), Proceedings of the 7th International Symposium on Mathematical Morphology, Paris, France, pp. 13–22, 2005.

  52. J.S. Lim, Two-Dimensional Signal and Image Processing, Prentice-Hall, Englewood Cliffs, New-Jersey, USA, 1990.

    Google Scholar 

  53. T. Lindeberg, “Scale-space theory: A basic tool for analysing structures at different scales,” Journal of Applied Statistics, (Supplement on Advances in Applied Statistics: Statistics and Images: 2), Vol. 21, No. 2, pp. 225–270, 1994.

  54. W.A.J. Luxemburg and A.C. Zaanen, Riesz Spaces, North Holland, Amsterdam, Netherlands, 1971.

    MATH  Google Scholar 

  55. S.G. Mallat, “A theory for multiresolution decomposition: The wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, pp. 674–693, 1989.

    Article  MATH  Google Scholar 

  56. P.A. Maragos and R.W. Schafer, “Morphological filters, Part I: their set-theoretic analysis and relations to linear shift invariant filters,” IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 35, No. 8, pp. 1153–1169, 1987a.

    Article  MathSciNet  Google Scholar 

  57. P.A. Maragos and R.W. Schafer, “Morphological filters, Part II: Their relations to median order statistic, and stack filters,” IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 35, No. 8, pp. 1170–1184, 1987b.

    Article  MathSciNet  Google Scholar 

  58. D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, W.H. Freeman and Company: San Fransisco, USA, 1982.

    Google Scholar 

  59. G. Matheron, Eléments Pour Une théorie Des Milieux Poreux, Masson, Paris, 1967.

    Google Scholar 

  60. G. Matheron, Image Analysis and Mathematical Morphology. Volume 2: Theoretical Advances, chapt. Filters and Lattices, Academic Press: London, U.K., pp. 115–140, 1988.

  61. F. Mayet, J.C. Pinoli, and M. Jourlin, “Justifications physiques et applications du modèle LIP pour le traitement des images obtenues en lumière transmise,” Traitement du signal, Vol. 13, pp. 251–262, 1996.

    MATH  Google Scholar 

  62. F. Meyer and S. Beucher, “Morphological segmentation,” Journal of Visual Communication and Image Representation, Vol. 1, No. 1, pp. 21–46, 1990.

    Article  Google Scholar 

  63. M. Nagao and T. Matsuyama, “Edge preserving Smoothing,” Computer Graphics and Image Processing, Vol. 9, pp. 394–407, 1979.

    Article  Google Scholar 

  64. A.V. Oppenheim, “Superposition in a class of nonlinear systems,” Research Laboratory of Electronics, M.I.T., Cambridge, USA, Technical report, 1965.

  65. A.V. Oppenheim, “Generalized superposition,” Information and Control, Vol. 11, pp. 528–536, 1967.

    Article  MATH  Google Scholar 

  66. A.V. Oppenheim, “Nonlinear filtering of multiplied and convolved signals,” in Proceedings of the IEEE, 1968.

  67. R.B. Paranjape, R.M. Rangayyan, and W.M. Morrow, “Adaptive neighbourhood mean and median image filtering,” Electronic Imaging, Vol. 3, No. 4, pp. 360–367, 1994.

    Article  Google Scholar 

  68. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629–639, 1990.

    Article  Google Scholar 

  69. J.C. Pinoli, “Contribution à la modélisation, au traitement et à l’analyse d’image,” Ph.D. thesis, Department of Mathematics, University of Saint-Etienne, France, 1987.

  70. J.C. Pinoli, “A contrast definition for logarithmic images in the continuous setting,” Acta Stereologica, Vol. 10, pp. 85–96, 1991.

    MATH  Google Scholar 

  71. J.C. Pinoli, “Modélisation et traitement des images logarithmiques: Théorie et applications fondamentales,” Department of Mathematics, University of Saint-Etienne, Technical Report 6 (this report is a revised and expanded synthesis of the theoretical basis and several fundamental applications of the LIP approach published from 1984 to 1992. It has been reviewed by international referees and presented in December 1992 for passing the “Habilitation à Diriger des Recherches” French degree), 1992.

  72. J.C. Pinoli, “A general comparative study of the multiplicative homomorphic, log-ratio and logarithmic image processing approaches,” Signal Processing, Vol. 58, pp. 11–45, 1997a.

    Article  MATH  Google Scholar 

  73. J.C. Pinoli, “The logarithmic image processing model: Connections with human brightness perception and contrast estimators,” Journal of Mathematical Imaging and Vision, Vol. 7, No. 4, pp. 341–358, 1997b.

    Article  Google Scholar 

  74. I. Pitas and A.N. Venetsanopoulos, Nonlinear Digital Filters: Principles and Applications, Kluwer Academic: Norwell Ma., USA, 1990.

    MATH  Google Scholar 

  75. I. Pitas and A.N. Venetsanopoulos, “Order statistics in digital image processing,” in Proceedings of the IEEE, pp. 1893–1923, 1992.

  76. T.F. Rabie, R.M. Rangayyan, and R.B. Paranjape, “Adaptive-neighborhood image deblurring,” Electronic Imaging, Vol. 3, No. 4, pp. 368–378, 1994.

    Article  Google Scholar 

  77. G. Ramponi, N. Strobel, S.K. Mitra, and T.H. Yu, “Nonlinear unsharp masking methods for image-contrast enhancement,” Journal of Electronic Imaging, Vol. 5, No. 3, pp. 353–366, 1996.

    Article  Google Scholar 

  78. R.M. Rangayyan, M. Ciuc, and F. Faghih, “Adaptive-neighborhood filtering of images corrupted by signal-dependent noise,” Applied Optics, Vol. 37, No. 20, pp. 4477–4487, 1998.

    Article  Google Scholar 

  79. R.M. Rangayyan and A. Das, “Filtering multiplicative noise in images using adaptive region-based statistics,” Electronic Imaging, Vol. 7, No. 1, pp. 222–230, 1998.

    Article  Google Scholar 

  80. G.X. Ritter, “Recent developments in image algebra,” in, P. Hawkes (Ed.), Advances in Electronics and Electron Physics, New-York, USA, pp. 243–308, 1991.

  81. G.X. Ritter and J.N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, CRC Press, Boca Ration, FL, USA, 1996.

    MATH  Google Scholar 

  82. G.X. Ritter, J.N. Wilson, and J.L. Davidson, “Image algebra, an overview,” Computer Vision, Graphics, and Image Processing, Vol. 49, No. 3, pp. 297–331, 1990.

    Article  Google Scholar 

  83. M. Ropert and D. Pelé, “Synthesis of adaptive weighted order statistic filters with gradient algorithms,” in J. Serra and P. Soille (Eds.), Mathematical Morphology and its Applications to Image Processing, pp. 37–44, 1994.

  84. A. Rosenfeld, Picture Processing by Computers, Academic Press, New-York, USA, 1969.

    Google Scholar 

  85. P. Salembier, “Structuring element adaptation for morphological filters,” Journal of Visual Communication and Image Representation, Vol. 3, No. 2, pp. 115–136, 1992.

    Article  Google Scholar 

  86. P. Salembier and J. Serra, “Flat zones filtering, connected operators, and filters by reconstruction,” IEEE Transactions on Image Processing, Vol. 4, No. 8, pp. 1153–1159, 1995.

    Article  Google Scholar 

  87. W.F. Schreiber, Fundamentals of Electronic Imaging Systems: Some Aspects of Image Processing, Springer: Berlin, Germany 2nd. edn. edition, 1991.

    MATH  Google Scholar 

  88. J. Serra, “L’analyse des textures par la géométrie aléatoire,” Comptes-Rendus du Comité Scientifique de l’IRSID, 1965.

  89. J. Serra, Image Analysis and Mathematical Morphology, Academic Press: London, U.K., 1982.

    MATH  Google Scholar 

  90. J. Serra, Image Analysis and Mathematical Morphology. Volume 2: Theoretical Advances, chapt. Mathematical Morphology for Complete Lattices, Academic Press: London, U.K., pp. 13–35, 1988a.

  91. J. Serra, Image Analysis and Mathematical Morphology. Volume 2: Theoretical Advances, chapt. Introduction to Morphological Filters, pp. 101–114, Academic Press: London, U.K., 1988b.

  92. J. Serra, Image Analysis and Mathematical Morphology. Volume 2: Theoretical Advances, chapt. Alternating Sequential Filters, Academic Press: London, U.K., pp. 203–216, 1988.

  93. J. Serra and P. Salembier, “Connected operators and pyramids,” in Proceedings of SPIE, San Diego, USA, pp. 65–76 1993.

  94. H. Shvayster and S. Peleg, “Pictures as elements in a vector space,” in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Washington, USA, pp. 442–446, 1983.

  95. H Shvayster and S. Peleg, “Inversion of picture operators,” Pattern Recognition Letters, Vol. 5, pp. 49–61, 1987.

    Article  Google Scholar 

  96. P. Soille, Morphological Image Analysis. Principles and Applications, chapt. Introduction, Springer Verlag, New York, pp. 1–14, 2003a.

  97. P. Soille, Morphological Image Analysis. Principles and Applications, chapt. Filtering, Springer Verlag: New York, pp. 241–266, 2003b.

  98. W.J. Song and W.A. Pearlman, “Restoration of noisy images with adaptive windowing and nonlinear filtering,” in Visual Communication and Image Processing, pp. 198–206, 1986.

  99. T.G. Stockham, “The applications of generalized linearity to automatic gain control,” IEEE Transactions on Audio and Electroacoustics, Vol. AU-16, No. 2, pp. 267–270, 1968.

  100. T.G. Stockham, “Image processing in the context of a visual model,” in Proceedings of the IEEE, pp. 825–842, 1972.

  101. G. Strang, Linear Algebra and its Applications, Academic Press: New-York, USA, 1976.

    MATH  Google Scholar 

  102. F.K. Sun and P. Maragos, “Experiments on image compression using morphological pyramids,” in Proceedings of the SPIE Visual Communications and Image Processing, pp. 1303–1312, 1989.

  103. C. Vachier, “Morphological scale-space analysis and feature extraction,” in Proceedings of the IEEE International Conference on Image Processing, Thessaloniki, Greece, pp. 676–679, 2001.

  104. F.A. Valentine, Convex Sets, McGraw-Hill, New-York, USA, 1964.

    MATH  Google Scholar 

  105. J.G. Verly and R.L. Delanoy, “Some principles and applications of adaptive mathematical morphology for range imagery,” Optical Engineering, Vol. 32, No. 12, pp. 3295–3306, 1993.

    Article  Google Scholar 

  106. L. Vincent and P. Soille, “Watersheds in digital spaces: An efficient algorithm based on immersion simulations,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, pp. 583–589, 1991.

    Article  Google Scholar 

  107. R.C. Vogt, “A spatially variant, locally adaptive, background normalization operator,” in, J. Serra and P. Soille (Eds.), Mathematical Morphology and its Applications to Image Processing, pp. 45–52, 1994.

  108. M. Wertheimer, A sourcebook of Gestalt Psychology, chapt. Laws of Organization in Perceptual Forms, Hartcourt, Brace, pp. 71–88, 1938.

Download references

Author information

Authors and Affiliations

  1. Ecole Nationale Supérieure des Mines de Saint-Etienne, Centre Ingénierie et Santé (CIS), Laboratoire LPMG, UMR CNRS, 5148, France

    Johan Debayle & Jean-Charles Pinoli

Authors
  1. Johan Debayle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean-Charles Pinoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Johan Debayle.

Additional information

Johan Debayle received his Ph.D. degree in Image, Vision and Signal from the French graduate school ‘Ecole Nationale Supérieure des Mines de Saint-Etienne’ and the University of Saint-Etienne, France, in 2005. He is currently a postdoctoral scientist at the French National Institute for Research in Computer Science and Control (INRIA). His research interests include adaptive image processing and analysis.

Jean-Charles Pinoli received Master’s, Ph.D. and D. Sc. (Habilitation à Diriger des Recherches) degrees in Applied Mathematics in 1983, 1985 and 1992, respectively. From 1985 to 1989, he was member of the opto-electronics department of the Angenieux (Thales) company, Saint-Héand, France, where he pioneered researches in the field of digital imaging and artificial vision. In 1990, he joined the Corporate Research Center of the Pechiney Company, Voreppe, France and was member of the computational technologies department in charge of the imaging activities. Since 2001, he is full professor at the French graduate school ‘Ecole Nationale Supérieure des Mines de Saint-Étienne’. He leads the Image Processing and Pattern Analysis Group within the Engineering and Health research Center and the LPMG Laboratory, UMR CNRS 5148. His research interests and teaching include Image Processing, Image Analysis, Mathematical Morphology and Computer Vision.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Debayle, J., Pinoli, JC. General Adaptive Neighborhood Image Processing:. J Math Imaging Vis 25, 245–266 (2006). https://doi.org/10.1007/s10851-006-7451-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-006-7451-8

Keywords

Search

Navigation