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Keywords:
discrete distribution mixtures; Bernoulli mixture; Gaussian mixture; EM algorithm; multi-spectral texture modelling; BTF texture modelling
discrete distribution mixtures; Bernoulli mixture; Gaussian mixture; EM algorithm; multi-spectral texture modelling; BTF texture modelling
Summary:
During the last decade we have introduced probabilistic mixture models into image modelling area, which present highly atypical and extremely demanding applications for these models. This difficulty arises from the necessity to model tens thousands correlated data simultaneously and to reliably learn such unusually complex mixture models. Presented paper surveys these novel generative colour image models based on multivariate discrete, Gaussian or Bernoulli mixtures, respectively and demonstrates their major advantages and drawbacks on texture modelling applications. Our mixture models are restricted to represent two-dimensional visual information. Thus a measured 3D multi-spectral texture is spectrally factorized and corresponding multivariate mixture models are further learned from single orthogonal mono-spectral components and used to synthesise and enlarge these mono-spectral factor components. Texture synthesis is based on easy computation of arbitrary conditional distributions from the model. Finally single synthesised mono-spectral texture planes are transformed into the required synthetic multi-spectral texture. Such models can easily serve not only for texture enlargement but also for segmentation, restoration, and retrieval or to model single factors in unusually complex seven dimensional Bidirectional Texture Function (BTF) space models. The strengths and weaknesses of the presented discrete, Gaussian or Bernoulli mixture based approaches are demonstrated on several colour texture examples.
References:
[1] Bennett, J., Khotanzad, A.: Multispectral random field models for synthesis and analysis of color images. IEEE Trans. Pattern Analysis and Machine Intelligence 20 (1998), 3, 327–332. DOI 10.1109/34.667889
[2] Dana, K. J., Nayar, S. K., Ginneken, B. van, Koenderink, J. J.: Reflectance and texture of real-world surfaces. In: CVPR, IEEE Computer Society, 1997, pp. 151–157.
[3] Bonet, J. S. De: Multiresolution sampling procedure for analysis and synthesis of textured images. In: ACM SIGGRAPH 97, ACM Press, 1997, pp. 361–368.
[4] Dempster, A. P., Laird, N. M., Rubin, D. B.: Maximum likelihood from incomplete data via the em algorithm. J. Roy. Statist. Soc. B 39 (1977), 1, 1–38. MR 0501537 | Zbl 0364.62022
[5] Efros, A. A., Freeman, W. T.: Image quilting for texture synthesis and transfer. In: ACM SIGGRAPH 2001 (E. Fiume, ed.), ACM Press, 2001, pp. 341–346.
[6] Efros, A. A., Leung, T. K.: Texture synthesis by non-parametric sampling. In: Proc. Internat. Conf. on Computer Vision (2), Corfu 1999, pp. 1033–1038.
[7] Filip, J., Haindl, M.: Bidirectional texture function modeling: A state of the art survey. IEEE Trans. Pattern Analysis and Machine Intelligence 31(2009), 11, 1921–1940. DOI 10.1109/TPAMI.2008.246
[8] Grim, J.: On numerical evaluation of maximum likelihood estimates for finite mixtures of distributions. Kybernetika 18 (1982), 173–190. MR 0680154 | Zbl 0489.62028
[9] Grim, J., Haindl, M.: Texture modelling by discrete distribution mixtures. Comput. Statist. Data Anal. 43 (2003), 3–4, 603–615. DOI 10.1016/S0167-9473(02)00174-3 | MR 1968071
[10] Grim, J., Haindl, M., Somol, P., Pudil, P.: A Subspace approach to texture modelling by using Gaussian mixtures. In: Proc. 18th Internat. Conference on Pattern Recognition. ICPR 2006 (Y. Y. Tang, S. P. Wang, D. S. Yeung, H. Yan, and G. Lorette, eds.), Vol. II, IEEE Computer Society, Los Alamitos 2006, pp. 235–238.
[11] Haindl, M.: Texture synthesis. Quarterly 4 (1991), 4, 305–331. Zbl 0757.62051
[12] Haindl, M., Grim, J., Pudil, P., Kudo, M.: A hybrid btf model based on gaussian mixtures. In: Texture 2005. The 4th Internat. Workshop on Texture Analysis and Synthesis in Conjunction with ICCV2005 (M. Chantler and O. Drbohlav, eds.), Beijing 2005, Heriot-Watt University & IEEE, pp. 95–100.
[13] Haindl, M., Grim, J., Somol, P., Pudil, P., Kudo, M.: A Gaussian mixture-based colour texture model. In: Proc. 17th IAPR Internat. Conference on Pattern Recognition (J. Kittler, M. Petrou, and M. Nixon, eds.), Vol. III, Los Alamitos 2004, IEEE, pp. 177–180.
[14] Haindl, M., Havlíček, V.: Multiresolution colour texture synthesis. In: Proc. 7th Internat. Workshop on Robotics in Alpe-Adria-Danube Region (K. Dobrovodský, ed.), Bratislava 1998. ASCO Art, pp. 297–302.
[15] Haindl, M., Havlíček, V.: A multiresolution causal colour texture model. In: Advances in Pattern Recognition (F. J. Ferri, J. M. Inesta, A. Amin, and P. Pudil, eds.), Lecture Notes in Computer Science 1876, Chapter 1, Springer-Verlag, Berlin 2000, pp. pages 114 –122.
[16] Haindl, Michal, Filip, Jiří: Extreme compression and modeling of bidirectional texture function. IEEE Trans. Pattern Analysis and Machine Intelligence 29(2007), 10, 1859–1865. DOI 10.1109/TPAMI.2007.1139
[17] Haindl, Michal, Hatka, Martin: Near-regular texture synthesis. In: Computer Analysis of Images and Patterns (X. Jiang and N. Petkov, eds.), Lecture Notes in Computer Sci. 5720, Springer 2009, pp. 1138–1145.
[18] Haindl, Michal, Havlíček, Vojtěch, Grim, Jiří: Colour texture representation based on multivariate bernoulli mixtures. In: 10th Internat. Conference on Information Sciences, Signal Processing and their Applications (B. Boashash, R. Hamila, S. Hussain Shaikh Salleh, and S. Abd Rahman Abu Bakar, eds.), Kuala Lumpur 2010. IEEE, pp. 578–581.
[19] Haindl, Michal, Havlíček, Vojtěch, Grim, Jiří: Probabilistic discrete mixtures colour texture models. Lecture Notes in Computer Sci. 5197 (2008), 675–682. DOI 10.1007/978-3-540-85920-8_82
[20] Heeger, D. J., Bergen, J. R.: Pyramid based texture analysis/synthesis. In: ACM SIGGRAPH 95, ACM Press 1995, pp. 229–238.
[21] Koudelka, M. L., Magda, S., Belhumeur, P. N., Kriegman, D. J.: Acquisition, compression, and synthesis of bidirectional texture functions. In: Texture 2003: Third Internat. Workshop on Texture Analysis and Synthesis, Nice 2003, pp. 59–64.
[22] Nicodemus, F. E., Richmond, J. C., Hsia, J. J., Ginsburg, I. W., Limperis, T.: Geometrical Considerations and Nomenclature for Feflectance. NBS Monograph No. 160, National Bureau of Standards, U.S. Department of Commerce, Washington, D. C. 1977.
[23] Paget, R., Longstaff, I. D.: Texture synthesis via a noncausal nonparametric multiscale markov random field. IEEE Trans. Image Processing 7 (1998), 8, 925–932. DOI 10.1109/83.679446
[24] Sattler, M., Sarlette, R., Klein, R.: Efficient and realistic visualization of cloth. In: Eurographics Symposium on Rendering 2003.
[25] Xu, Y., Guo, B., Shum, H.: Chaos Mosaic: Fast and Memory Efficient Texture Synthesis. Technical Report MSR-TR-2000-32, Redmont 2000.
[26] Zelinka, S., Garland, M.: Towards real-time texture synthesis with the jump map. In: 13th European Workshop on Rendering 2002, pp. 99–104.
[27] Zhu, S. C., Liu, X. W., Wu, Y. N.: Exploring texture ensembles by efficient markov chain monte carlo – toward a “trichromacy” theory of texture. IEEE Trans. Pattern Analysis and Machine Intelligence 22 (2000), 6, 554–569. DOI 10.1109/34.862195
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