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BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access December 30, 2016

Morphological probabilistic hierarchies for texture segmentation

  • Dominique Jeulin


A general methodology is introduced for texture segmentation in binary, scalar, or multispectral images. Textural information is obtained from morphological operations on images. Starting from a fine partition of the image in regions, hierarchical segmentations are designed in a probabilistic framework by means of probabilistic distances conveying the textural or morphological information, and of random markers accounting for the morphological content of the regions and of their spatial arrangement. The probabilistic hierarchies are built from binary or multiple fusion of regions.


[1] Alpert S,Galun M, Basri R, Brandt A. (2007) Image segmentation by probabilistic bottom-up aggregation and cue integration. In Computer Vision and Pattern Recognition, CVPR’07. IEEE Conference, 1-8 10.1109/CVPR.2007.383017Search in Google Scholar

[2] Angulo J, Jeulin D (2007) Stochastic watershed segmentation, Proc. ISMM’2007, 8th International Symposium on Mathematical Morphology, Rio de Janeiro, Brazil, October 10-13 2007, Banon G., Barrera J., Braga-Neto U. (eds), ISBN 978-85-17- 00032-4, 265-276 Search in Google Scholar

[3] Aubert A, Jeulin D (2000) Classification morphologique de surfaces rugueuses, Revue de Métallurgie - CIT/Sience et Génie des Matériaux Feb 2000 (2000), 253–262 10.1051/metal/200097020247Search in Google Scholar

[4] Benzecri JP (1973) L’analyse des données, Dunod, Paris, TIB nº3, §3-4, pp. 133-149; TIB nº4, §2.3, pp. 180-183 Search in Google Scholar

[5] Beucher S, Lantuéjoul Ch (1979) Use of watersheds in contour detection. In: International workshop on image processing, real-time edge and motion detection Search in Google Scholar

[6] Cord A, Jeulin D, Bach F (2007) Segmentation of random textures by morphological and linear operators, Proc. ISMM’2007, 8th International SymposiumonMathematical Morphology, Rio de Janeiro, Brazil, October 10-13 2007, Banon G., Barrera J., Braga-Neto U. (eds), ISBN 978-85-17-00032-4, 387-398 Search in Google Scholar

[7] Cord A, Bach F, Jeulin D (2010) Texture classification by statistical learning from morphological image processing. Application to metallic surfaces, Journal of Microscopy, 239, 159-166 Search in Google Scholar

[8] Duda RO, Hart PE (1973) Pattern recognition and scene analysis, Wiley, New York, 236-237 Search in Google Scholar

[9] Gillibert L, Jeulin D (2011) Stochastic Multiscale Segmentation Constrained by Image Content, Proc. ISSM 2011, Soille P., Pesaresi M., Ouzounis G.K. (eds), Lecture Notes in Computer Science 6671, Springer Verlag, 132-142 Search in Google Scholar

[10] Gillibert L, Peyrega Ch, Jeulin D, Guipont V, Jeandin M (2012) 3D Multiscale Segmentation and Morphological Analysis of X-ray Microtomography from Cold-sprayed Coatings, Journal of Microscopy, vol. 248, Part 2, 187-199. doi: 10.1111/j.1365- 2818.2012.03655.x Search in Google Scholar

[11] Gratin C, Vitria J, Moreso F, Seron D (1994) Texture classification using neural networks and local granulometries. In: Mathematical Morphology and its Applications to Image Processing, J. Serra and P. Soille (eds), Kluwer Academic Publishers, 309-316 10.1007/978-94-011-1040-2_40Search in Google Scholar

[12] Hastie T, Tibshirani R, Friedman J (2001) The Elements of Statistical Learning, Data Mining, Inference, and Prediction, Springer pp. 544-547 Search in Google Scholar

[13] Hummel RA, Zucker SW (1983) On the Foundations of Relaxation Labeling Processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-5, Nº 3, 267-287 Search in Google Scholar

[14] Jeulin D (1991) Modèles Morphologiques de Structures Aléatoires et de Changement d’Echelle. Thèse de Doctorat d’Etat ès Sciences Physiques, Université de Caen Search in Google Scholar

[15] Jeulin D (2008) Remarques sur la segmentation probabiliste, N-10/08/MM, Internal report, CMM, Mines ParisTech Search in Google Scholar

[16] Jeulin D, MoreaudM(2008) Segmentation of 2D and 3D textures from estimates of the local orientation, Image Analysis and Stereology, Vol. 27, 183-192 Search in Google Scholar

[17] Jeulin D (2015) Probabilistic Hierarchical Morphological Segmentation of Textures. In: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, J. A. Benediktsson, J. Chanussot, L. Najman, H. Talbot (eds), Springer International Publishing, 313-324 10.1007/978-3-319-18720-4_27Search in Google Scholar

[18] Karoui I, Fablet R, Boucher J M, Augustin JM(2006) Region-based image segmentation using texture statistics and level-set methods. In Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference, Vol. 2 10.1109/WISP.2007.4447617Search in Google Scholar

[19] Karoui I, Fablet R, Boucher J M, Pieczynski W, Augustin J M (2008) Fusion of textural statistics using a similarity measure: application to texture recognition and segmentation, Pattern Analysis and Applications, 11(3-4), 425-434 10.1007/s10044-008-0108-zSearch in Google Scholar

[20] Manjunath BS, Simchony T, Chellappa R (1990) Stochastic and Deterministic Networks for Texture Segmentation, IEEE Transactions on Acoustics, Speech, and Signal ProcessingVol. 38, Nº 6, 1039-1049 Search in Google Scholar

[21] Matheron G (1967) Eléments pour une théorie des milieux poreux, Masson, Paris Search in Google Scholar

[22] Matheron G (1975) Random sets and integral geometry, Wiley, New York Search in Google Scholar

[23] Meyer F, Beucher S (1990) Morphological segmentation, Journal of Visual Communication and Image Representation, Volume 1, Issue 1, 21–46 10.1016/1047-3203(90)90014-MSearch in Google Scholar

[24] Meyer F, Stawiaski J (2010) A stochastic evaluation of the contour strength, DAGM-Symposium’10, 513-522 10.1007/978-3-642-15986-2_52Search in Google Scholar

[25] Noyel G, Angulo J, Jeulin D (2007) Morphological segmentation of hyperspectral images, Image Analysis and Stereology, Vol. 26, 101-109 Search in Google Scholar

[26] Noyel, G, Angulo J, Jeulin D (2008) Classification-driven stochastic watershed. Application to multispectral segmentation, Proc. Fourth European Conference on Color in Graphics, Imaging and Vision (CGIV 2008), 471 - 476 Search in Google Scholar

[27] Noyel G, Angulo J., Jeulin D (2010) A new spatio-spectral morphological segmentation for multispectral remote sensing images, International Journal of Remote Sensing, Vol. 31, Issue 22, 5895-5920 Search in Google Scholar

[28] Peleg S (1980) A New Probabilistic Relaxation Scheme, IEEE Transactions on Pattern Analysis andMachine Intelligence, Vol. PAMI-2, Nº 4, 362-369 10.1109/TPAMI.1980.4767035Search in Google Scholar

[29] Pesaresi M, Benediktsson J (2001) A new approach for the morphological segmentation of high resolution satellite imagery, Geoscience and Remote Sensing, Vol. 39 , Issue 2, 309–320 Search in Google Scholar

[30] Rosenfeld A, Hummel RA, Zucker SW (1976) Scene Labeling by Relaxation Operations, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-6, Nº 6, 420-433 Search in Google Scholar

[31] Serra J (1982) Image Analysis and Mathematical Morphology, Academic Press, London Search in Google Scholar

[32] Sivakumar K, Goutsias J (1994) Monte Carlo estimation of morphological granulometric discrete size distributions. In:Mathematical Morphology and its Applications to Image Processing, J Serra and P Soille (eds), Kluwer Academic Publishers, 233-240 10.1007/978-94-011-1040-2_30Search in Google Scholar

Received: 2016-7-20
Accepted: 2016-2-11
Published Online: 2016-12-30

© 2016 Dominique Jeulin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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