Abstract
Textures are among the most important features in image analysis. This paper presents a novel methodology to extract information from them, converting an image into a simplified dynamical system in gravitational collapse whose states are described by using the lacunarity method. The paper compares the proposed approach to other classical methods using Brodatz’s textures as benchmark.
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References
Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)
Bala, J.W.: Combining structural and statistical features in a machine learning technique for texture classification. In: IEA/AIE, vol. 1, pp. 175–183 (1990)
Tuceryan, M., Jain, A.K.: Texture analysis. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) Handbook of Pattern Recognition and Computer Vision, pp. 235–276. World Scientific, Singapore (1993)
Casanova, D., Sá Junior, J.J.M., Bruno, O.M.: Plant leaf identification using gabor wavelets. International Journal of Imaging Systems and Technology 19(1), 236–243 (2009)
Lu, C.S., Chung, P.C., Chen, C.F.: Unsupervised texture segmentation via wavelet transform. Pattern Recognition 30(5), 729–742 (1997)
Arivazhagan, S., Ganesan, L.: Texture classification using wavelet transform. Pattern Recognition Letters 24(9-10), 1513–1521 (2003)
Costa, L.F., Rodrigues, F.A., Travieso, G., Villas Boas, P.R.: Characterization of complex networks: A survey of measurements. Advances in Physics 56(1) (2005)
Backes, A.R., Gonçalves, W.N., Martinez, A.S., Bruno, O.M.: Texture analysis and classification using deterministic tourist walk. Pattern Recognition 43, 685–694 (2010)
Varma, M., Garg, R.: Locally invariant fractal features for statistical texture classification. In: International Conference on Computer Vision - ICCV 2007, pp. 1–8 (2007)
Newton, I.: Philosophiae Naturalis Principia Mathematica. University of California, Berkeley (1999); original 1687, translation guided by I.B. Cohen
Mandelbrot, B.: The fractal geometry of nature. Freeman, San Francisco (1982)
Allain, C., Cloitre, M.: Characterizing the lacunarity of random and deterministic fractal sets. Phys. Rev. A 44(6), 3552–3558 (1991)
Facon, J., Menoti, D., de Albuquerque Araújo, A.: Lacunarity as a texture measure for address block segmentation. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 112–119. Springer, Heidelberg (2005)
Dong, P.: Test of a new lacunarity estimation method for image texture analysis. International Journal of Remote Sensing 21(17), 3369–3373 (2000)
Du, G., Yeo, T.S.: A novel lacunarity estimation method applied to SAR image segmentation. IEEE Trans. Geoscience and Remote Sensing 40(12), 2687–2691 (2002)
Brodatz, P.: Textures: A photographic album for artists and designers. Dover Publications, New York (1966)
Everitt, B.S., Dunn, G.: Applied Multivariate Analysis, 2nd edn. Arnold (2001)
Azencott, R., Wang, J.P., Younes, L.: Texture classification using windowed fourier filters. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 148–153 (1997)
Idrissa, M., Acheroy, M.: Texture classification using gabor filters. Pattern Recognition Letters 23(9), 1095–1102 (2002)
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© 2011 Springer-Verlag Berlin Heidelberg
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de M. Sá Junior, J.J., Backes, A.R. (2011). A Simplified Gravitational Model for Texture Analysis. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_4
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DOI: https://doi.org/10.1007/978-3-642-23672-3_4
Publisher Name: Springer, Berlin, Heidelberg
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