Multi-scale morphological modeling of a class of structural texture

• Autorzy: Zaremba M. B. , Palenichka R. M. , Missaoui R.
• Języki publikacji: EN

Abstrakty

EN

Consistent and time-efficient modeling of textures is important both for realistic texture mapping in computer graphics and correct texture segmentation in computer vision. A large class of natural and artificial images is represented by the so-called structural textures, which contain visibly repetitive patterns. The multi-scale morphological modeling approach proposed in this paper explicitly describes shape and intensity parameters of structural textures. It is based on a cellular growth of a texture region by a sequential morphological generation of structural texture cells starting from a seed cell. Its main advantage is a concise shape representation for structural texture cells in the form of piecewise linear skeletons. Another advantage is a robust and computationally efficient estimation of texture parameters. The cell parameter estimation is based on the cell localization and adaptive segmentation using a multi-scale matched filter. The experiments reported in the paper are related to texture parameter estimation from synthetic and real textures as well as structural texture synthesis based on the estimated parameters.

Słowa kluczowe

EN

texture modeling; structural texture; local scale; mathematical morphology; parameter estimation; binarization

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2005
• Tom: Vol. 14, No. 2
• Strony: 171--199
• Opis fizyczny: Bibliogr. 46 poz., rys., wykr.

Twórcy

autor

Zaremba M. B.

• Dept. of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, Québec, Canada

autor

Palenichka R. M.

• Dept. of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, Québec, Canada

autor

Missaoui R.

• Dept. of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, Québec, Canada

Bibliografia

• [1] Brodatz P.: Textures: A Photographic Album for Artists and Designers. Dover: New York, 1966.
• [2] Haralick R. M., Shanmugam K. and Dinstein I.: Texture features for image classification. IEEE Trans. System Man Cybernetics, 8 (6), 610-621, 1973.
• [3] Zucker S.W.: Toward a model of texture. Computer Graphics and Image Processing, 5, 190-202, 1976.
• [4] Yokoyama R., Haralick R. M.: Texture synthesis using a growth model. Computer Graphics and Image Processing, 8 , 369-381, 1978.
• [5] Haralick R. M.: Statistical and structural approaches to textures. Proc. IEEE, 67, 786-804, 1979.
• [6] Ballard D. and Brown C.: Computer Vision. Prentice Hall: Englewood Cliffs, 1982.
• [7] Matsuyama T., Saburi K., and Nagao M.: A structural analyzer for regularly arranged textures. Computer Graphics and Image Processing, 18, 259-278, 1982.
• [8] Serra J.: Image Analysis and Mathematical Morphology. Academic Press: London, 1982.
• [9] Tomita F., Shirai Y., and Tsuji S.: Description of textures by a structural analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 4(2), 183-191, 1982.
• [10] Wechsler H.: Texture analysis: A survey. Signal Processing, 2, 271-282, 1982.
• [11] Cross G. R., Jain A. K.: Markov random field texture models. IEEE Trans. PAMI, 5(1), 25-39, 1983.
• [12] Julesz B., Bergen J. R.: Textons, the fundamental elements in pre-attentive vision and perception of textures. Bell Syst. Tech. J., 62, 1619-1645, 1983.
• [13] Heckbert P.: Survey of texture mapping. IEEE Comp. Graphics and Applications, 6 , 321-332, 1986.
• [14] Julesz B.: Textons gradients: the texton theory revisited. Biological Cybernetics, 54, 247-251, 1986.
• [15] Magnenat-Thalmann N., Thalmann D.: Image Synthesis: Theory and Practice. Springer-Verlag: New York, London, Paris, 1987.
• [16] Sahoo P. K. et al.: A survey of thresholding techniques. Computer Vision, Graphics and Image Process., 41, 233-260, 1988.
• [17] Fogel I., Sagi D.: Gabor filters as texture discriminator. Biological Cybernetics, 61, 103-113, 1989.
• [18] Maragos P.: Pattern spectrum and multi-scale shape representation. IEEE Trans. PAMI, 11(7), 701-717, 1989.
• [19] Unser M., Eden M.: Multi-resolution feature extraction and selection for texture segmentation. IEEE Trans. PAMI, 11, 717-728, 1989.
• [20] Bovik A. C., Clark M., and Geisler W. S.: Multi-channel texture analysis using localized spatial filters. IEEE Trans. PAMI, 12(1), 55-73, 1990.
• [21] Malik J., Perona P.: Preattentive texture discrimination with early vision mechanisms. J. Optical Society of America, 7(2), 933-932, 1990.
• [22] Freeman W. T., Adelson E. H.: The design and use of steerable filters. IEEE Trans. PAMI, 13(9), 891-426, 1991.
• [23] Meer et al. P.: Robust regression methods for Computer vision: a review. Int. Journal of Computer Vision, 6(1), 59-70, 1991.
• [24] Jain A. K., Dubuisson M.-P.: Segmentation of X-ray and C-scan images of fiber reinforced com posite materials. Pattern Recognition, 25(3), 257-270, 1992.
• [25] Mao J., Jain A. K.: Texture classification and segmentation using multi-resolution simultaneous autoregressive models. Pattern Recognition, 25(2), 173-188, 1992.
• [26] Gauch J. M., Pizer S. M.: The intensity axis of symmetry and its application to image segmentation. IEEE Trans. PAMI, 15(8), 753-761, 1993.
• [27] Li B.-C., Shen J.: Two-dimensional local moments, surface fitting and their fast computation. Pattern Recognition, 27, 785-790, 1994.
• [28] Lindenberg T.: Scale-Space Theory in Computer Vision, Kluwer Academic Publishers, 1994.
• [29] Heeger D. J., Bergen J. R.: Pyramid-based texture analysis/synthesis. ACM Proc. SIGGRAPH’1995, 229-238, 1995.
• [30] Unser M.: Texture classification and segmentation using wavelet frames. IEEE Trans. Image Processing, 4(11), 1549-1560, 1995.
• [31] Azencott R., Wang J., and Younes L.: Texture classification using windowed Fourier filters. IEEE Trans. PAMI, 19(2), 148-153, 1997.
• [32] De Bonet J. S.: Multiresolution sampling procedure for analysis and synthesis of texture images. ACM Proc. SIGGRAPHT 997, 361-368, 1997.
• [33] Eom K. B.: 2-D moving average models for texture synthesis and analysis. IEEE Trans. Image Processing, 7(12), 1741-1746, 1998.
• [34] Palenichka R.M., Zinterhof P., Rytsar Yu. B., Ivasenko I. B.: Structure-adaptive image filtering using order statistics. Journal of Electronic Imaging, 2, 339-349, 1998.
• [35] Efros A. A., Leung T. K.: Texture synthesis by non-parametric sampling. Proc. 7th IEEE Int. Conf. Computer Vision (ICCV’99), 2, 1033-1038, 1999.
• [36] Kaplan L. M.: Extended fractal analysis for texture classification and segmentation. IEEE Trans. Image Processing, 8(11), 1572-1585, 1999.
• [37] Randen T., Husoy J. H.: Filtering for texture classification: a comparative study. IEEE Trans. PAMI, 21(4), 291-310, 1999.
• [38] Lefebvre L. and Poulin P.: Analysis and synthesis of structural textures. Proc. Int. Conf. Graphics Interface’2000, 77-86, 2000.
• [39] Wei L. Y., Levoy M.: Fast texture synthesis using tree-structured vector quantization. ACM Proc. SIGGRAPH’2000, 479-488, 2000.
• [40] Di Gesù V., Palenichka R. M.: Fast recursive computation of local axial moments. Signal Processing, 81, 265-273, 2001.
• [41] Malik J. et al: Contour and texture analysis for image segmentation. Int. Journal of Computer Vision, 43(1), 7-27, 2001.
• [42] Saha P. K., Udupa J. K.: Optimum image thresholding via class uncertainty and region homogeneity. IEEE Trans. PAMI, 23(7), 689-706, 2001.
• [43] Gimelfarb G.: Estimation of texels for regular mosaics using model-based interaction maps. Proc. Workshop SSPR&SPR 2002, LNCS 2396, 177-185, 2002.
• [44] Lee K.-L., Chen L.-H.: A new method for extracting primitives of regular textures based on wavelet transform. Int. Journal of Pattern Recognition and Artificial Intelligence, 16(1), 1-25, 2002.
• [45] Palenichka R. M.: A visual attention operator based on morphological models of images and maximum likelihood decision. Proc. Workshop SSPR&SPR 2002, LNCS 2396, 310-319, 2002.
• [46] Palenichka R. M., Zaremba M. B.: A fast algorithm for the computation of axial moments and its application to the orthogonal fitting of curves. Pattern Recognition, 36(7), 1519-1528, 2003.