Abstract
A novel method of texture characterization, called intersize correlation of grain occurrences, is proposed. This idea is based on a model of texture description, called “Primitive, Grain and Point Configuration (PGPC)” texture model. This model assumes that a texture is composed by arranging grains, which are locally extended objects appearing actually in a texture. The grains in the PGPC model are regarded to be derived from one primitive by the homothetic magnification, and the size of grain is defined as the degree of magnification. The intersize correlation is the correlation between the occurrences of grains of different sizes located closely to each other. This is introduced since homothetic grains of different sizes often appear repetitively and the appearance of smaller grains depends on that of larger grains. Estimation methods of the primitive and grain arrangement of a texture are presented. A method of estimating the intersize correlation and its application to texture regeneration are presented with experimental results. The regenerated texture has the same intersize correlation as the original while the global arrangement of large-size grains are completely different. Although the appearance of the resultant texture is globally different from the original, the semi-local appearance in the neighborhood of each largesize grain is preserved.
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References
Mahamadou I., and Marc A. (2002). “Texture classification using Gabor filters,” Pattern Recognition Letters, Vol 23, 1095–1102.
Liu, F., and Picard, R. W. (1996). “Periodicity, Directionality, and Randomness: Wold features for image modeling and retrieval,” IEEE Trans. Pattern Anal. Machine Intell., Vol. 18, 722–733.
Tomita, T., Shirai, Y., and Tsuji. Y. (1982). “Description of Textures by a Structural Analysis,” IEEE Trans. Pattern Anal. Machine Intell., Vol. PAMI-4, 183–191.
Sand, F., and Dougherty, E. R. (1998). “Asymptotic granulometric mixing theorem: morphological estimation of sizing parameters and mixture proportions,” Pattern Recognition, Vol. 31, no. 1, 53–61.
Balagurunathan, Y., and Dougherty, E. R. (2003). “Granulometric parametric estimation for the random Boolean model using optimal linear filters and optimal structuring elements,” Pattern Recognition Letters, Vol. 24., 283–293.
Gimel’farb, G. (2001). “Characteristic integration structures in Gibbs texture modelling,” in Imaging and Vision Systems: Theory, Assesment and Applications J. Blanc-Talon and D. Popescu, Ed. Huntington: Nova Science Publishers, 71–90.
Aubert, A. and Jeulin, D. (2000). “Estimation of the influence of second and third order moments on random sets reconstruction,” Pattern Recognition, Vol. 33, no. 6, 1083–1104.
Asano, A., Ohkubo, T., Muneyasu, M., and Hinamoto T. (2003). “Primitive and Point Configuration Texture Model and Primitive Estimation using Mathematical Morphology,” Proc. 13th Scandinavian Conference on Image Analysis, Göteborg, Sweden; Springer LNCS Vol. 2749, 178–185.
Heijmans, H. J. A. M. (1994). Morphological Image Operators, San Diego: Academic Press.
Maragos P. (1989). “Pattern Spectrum and Multiscale Shape Representation,” IEEE Trans. Pattern Anal. Machine Intell. Vol. 11, 701–716.
Soille, P. (2003). Morphological Image Analysis, 2nd Ed. Berlin: Springer.
Li, W., Hagiwara, I., Yasui, T., and Chen, H. (2003). “A method of generating scratched look calligraphy characters using mathematical morphology,” Journal of Computational and Applied Mathematics, Vol. 159, 85–90.
Serra, J. Ed. (1988). Image Analysis and Mathematical Morphology Volume 2. Technical Advances, Academic Press, London, Chaps. 15 and 18.
Lantuejoul, Ch. (1980). “Skeletonization in quantitative metallography,” Issues in Digital Image Processing, R. M. Haralick and J. C. Simon, Eds., Sijthoof and Noordoff.
Serra. J. (1982). Image analysis and mathematical morphology. London: Academic Press.
Kobayashi. Y., and Asano. A. (2003). “Modification of spatial distribution in Primitive and Point Configuration texture model,” Proc. 13th Scandinavian Conference on Image Analysis, Göteborg, Sweden; Springer LNCS Vol. 2749, 877–884.
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Asano, A., Kobayashi, Y., Muraki, C. (2005). Intersize Correlation of Grain Occurrences in Textures and Its Application to Texture Regeneration. In: Ronse, C., Najman, L., Decencière, E. (eds) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3443-1_32
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DOI: https://doi.org/10.1007/1-4020-3443-1_32
Publisher Name: Springer, Dordrecht
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