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International Conference on Computer Analysis of Images and Patterns

CAIP 2011: Computer Analysis of Images and Patterns pp 26–33Cite as

A Simplified Gravitational Model for Texture Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6854))

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

  1. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. 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)

    Chapter  Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. Lu, C.S., Chung, P.C., Chen, C.F.: Unsupervised texture segmentation via wavelet transform. Pattern Recognition 30(5), 729–742 (1997)

    Article  Google Scholar 

  6. Arivazhagan, S., Ganesan, L.: Texture classification using wavelet transform. Pattern Recognition Letters 24(9-10), 1513–1521 (2003)

    Article  MATH  Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Article  MATH  Google Scholar 

  9. Varma, M., Garg, R.: Locally invariant fractal features for statistical texture classification. In: International Conference on Computer Vision - ICCV 2007, pp. 1–8 (2007)

    Google Scholar 

  10. Newton, I.: Philosophiae Naturalis Principia Mathematica. University of California, Berkeley (1999); original 1687, translation guided by I.B. Cohen

    MATH  Google Scholar 

  11. Mandelbrot, B.: The fractal geometry of nature. Freeman, San Francisco (1982)

    MATH  Google Scholar 

  12. Allain, C., Cloitre, M.: Characterizing the lacunarity of random and deterministic fractal sets. Phys. Rev. A 44(6), 3552–3558 (1991)

    Article  MathSciNet  Google Scholar 

  13. 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)

    Chapter  Google Scholar 

  14. Dong, P.: Test of a new lacunarity estimation method for image texture analysis. International Journal of Remote Sensing 21(17), 3369–3373 (2000)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Brodatz, P.: Textures: A photographic album for artists and designers. Dover Publications, New York (1966)

    Google Scholar 

  17. Everitt, B.S., Dunn, G.: Applied Multivariate Analysis, 2nd edn. Arnold (2001)

    Google Scholar 

  18. Azencott, R., Wang, J.P., Younes, L.: Texture classification using windowed fourier filters. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 148–153 (1997)

    Article  Google Scholar 

  19. Idrissa, M., Acheroy, M.: Texture classification using gabor filters. Pattern Recognition Letters 23(9), 1095–1102 (2002)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Departamento de Engenharia de Teleinformática - DETI, Centro de Tecnologia - UFC, Campus do Pici, S/N, Bloco 725, Caixa Postal 6007, CEP: 60.455-970, Fortaleza, Brasil

    Jarbas J. de M. Sá Junior

  2. Faculdade de Computação, Universidade Federal de Uberlândia, Av. João Naves de Ávila, 2121, 38408-100, Uberlândia, MG, Brasil

    André R. Backes

Authors
  1. Jarbas J. de M. Sá Junior
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  2. André R. Backes
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Editor information

Editors and Affiliations

  1. Dpto. Matematica Aplicada I, Escuaela Técnica Superior de Ingeniería Informática, Universite de Sevilla, Avda. Reina Mercedes, s/n, 41012, Sevilla, Spain

    Pedro Real

  2. Departamento de Matemática Aplicada I, Escuela Técnica Superior de Ingeniería Informática, University of Seville, Avenida Reina Mercedes s/n, 41012, Sevilla, Spain

    Daniel Diaz-Pernil  & Helena Molina-Abril  & 

  3. Departamento de Didáctica de la Mathemática y de las CC.Experimentales, Universidad del País Vasco-Esukal Herriko Unibertsitatea, Escuela Universitaria de Magisterio, Ramón y Cajal, 72, 48014, Bilbao (Bizcaia), Spain

    Ainhoa Berciano

  4. Institute of Computer Graphics and Algorithms, Pattern Recognition and Image Processing Group, Vienna University of Technology, Favoritenstraße 9/186-3, 1040, Vienna, Austria

    Walter Kropatsch

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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

  • Print ISBN: 978-3-642-23671-6

  • Online ISBN: 978-3-642-23672-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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