Abstract
Our purpose is to extend the Local Binary Pattern method to three dimensions and compare it with the two-dimensional model for three-dimensional texture analysis. To compare these two methods, we made classification experiments using three databases of three-dimensional texture images having different properties. The first database is a set of three-dimensional images without any distorsion or transformation, the second contains additional gaussian noise. The last one contains similar textures as the first one but with random rotations according x, y and z axis. For each of these databases, the three-dimensional Local Binary Pattern method outperforms the two-dimensional approach which has more difficulties to provide correct classifications.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tuceryan, M., Jain, A.K.: 2.1. Texture Analysis. The Handbook of Patern Recognition and Computer Vision, 207–248 (1998)
Haralick, R.M.: Statistical and structural approaches to textures. Proceedings of the IEEE 67(5), 786–804 (1979)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973)
Wang, L., He, D.C.: Texture classification using texture spectrum. Pattern Recognition 23(8), 905–910 (1990)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)
Tuceryan, M., Jain, A.K.: Texture segmentation using voronoi polygons. IEEE Transactions On Pattern Analysis And Machine Intelligence 12, 211–216 (1990)
Chellappa, R., Jain, A.K.: Markov Random Fields Theory and Application. Academic Press, London (1993)
Mosquera, A., Cabello, D., Carreira, M., Penedo, M.: A fractal-based approach to texture segmentation. In: ICIPA 1992: Proceedings on the International Conference on Image Processing and its Application (1992)
Turner, M.: Texture discrimination by gabor functions. Biological Cybernetics 55, 71–82 (1986)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE transaction on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)
Laine, A., Fan, J.: Textures classification by wavelet packets signatures. IEEE Transaction on Patern Analysis and Machine Intelligence 15, 1186–1191 (1993)
Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing 4, 1549–1560 (1995)
Stachowiak, G.P., Podsiadlo, P., Stachowiak, G.W.: A comparison of texture feature extraction methods for machine condition monitoring and failure analysis. Tribology Letters 20(2), 133–147 (2005)
Suzuki, M.T., Yoshitomo, Y., Osawa, N., Sugimoto, Y.: Classification of solid textures using 3d mask patterns. In: ICSMC 2004: International Conference on Systems, Man and Cybernetics (2004)
Kovalev, V.A., Petrou, M., Bondar, Y.S.: Texture anisotropy in 3d images. IEEE Transaction on Image Processing 8, 346–360 (1999)
Ghoneim, D.M., Toussaint, G., Constans, J.M., de Certaines, J.D.: Three dimensional texture analysis in mri: a preliminary evaluation in gliomas. Magnetic Resonance Imaging 21, 983–987 (2003)
Showalter, C., Clymer, B.D., Richmond, B., Powell, K.: Three-dimensional texture analysis of cancellous bone cores evaluated at clinical ct resolutions. Osteoporos Int. 17, 259–266 (2006)
Chen, X., Murphy, R.F.: Robust classification of subcellular location patterns in high resolution 3d fluorescence microscope images. In: EMBS 2004: Proceedings of the 26th Annual International Conference of the IEEE EMBS, pp. 1–5 (2004)
Jafari-Khouzani, K., Soltanian-Zadeh, H., Elisevich, K., Patel, S.: Comparison of 2d and 3d wavelet features for tle lateralization. In: Proceedings of the SPIE, vol. 5369 (2004)
Zhan, Y., Shen, D.: Deformable segmentation of 3d ultrasound prostate image using statistical texture matching method. IEEE transaction on medical imaging 25(3), 256–272 (2006)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transaction on Pattern Analysis and Machine Intelligence 29(6), 915–928 (2007)
Ojala, T., Pietikäinen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE transaction on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Kopf, J., Fu, C.W., Cohen-Or, D., Deussen, O., Lischinski, D., Wong, T.T.: Solid texture synthesis from 2d exemplars. In: SIGGRAPH 2007: Proceedings of the 34th International Conference and Exhibition on Computer Graphics and Interactive Techniques (2007)
Gool, L.J.V., Dewaele, P., Oosterlinck, A.: Texture analysis anno 1983. Computer Vision, Graphics, and Image Processing 29(3), 336–357 (1985)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paulhac, L., Makris, P., Ramel, JY. (2008). Comparison between 2D and 3D Local Binary Pattern Methods for Characterisation of Three-Dimensional Textures. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_66
Download citation
DOI: https://doi.org/10.1007/978-3-540-69812-8_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69811-1
Online ISBN: 978-3-540-69812-8
eBook Packages: Computer ScienceComputer Science (R0)