Advertisement

Bimodal Texture Segmentation with the Lee-Seo Model

  • Michalis A. Savelonas
  • Dimitris K. Iakovidis
  • Dimitris Maroulis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4633)

Abstract

This paper presents a novel approach to bimodal texture segmentation. The proposed approach features a local binary pattern-based scheme to transform bimodal textures into bimodal gray-scale intensities, segmentable by the Lee-Seo active contour model. This process avoids the iterative calculation of active contour equation terms derived from textural feature vectors, thus reducing the associated computational overhead. The proposed approach is region-based and invariant to the initialization of the level-set function, as it converges to a stationary global minimum. It is experimentally validated on 18 composite texture images of the Brodatz album, obtaining high quality segmentation results, whereas the convergence times are up to an order of magnitude smaller than the ones reported for other active contour approaches for texture segmentation.

Keywords

Texture Segmentation Local Binary Patterns Active Contours 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Paragios, N., Deriche, R.: Geodesic Active Contours for Supervised Texture Segmentation. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2422–2427. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  2. 2.
    Aujol, J.F., Aubert, G., Blanc-Feraud, L.: Wavelet-Based Level Set Evolution for Classification of Textured Images. IEEE Transactions on Image Processing 12(12), 1634–1641 (2003)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Sagiv, C., Sochen, N.A., Zeevi, Y.: Integrated Active Contours for Texture Segmentation. IEEE Transactions on Image Processing 1(1), 1–19 (2004)Google Scholar
  4. 4.
    He, Y., Luo, Y., Hu, D.: Unsupervised Texture Segmentation via Applying Geodesic Active Regions to Gaborian Feature Space. IEEE Transactions on Engineering, Computing and Technology, 272–275 (2004)Google Scholar
  5. 5.
    Pujol, O., Radeva, P.: Texture Segmentation by Statistic Deformable Models. International Journal of Image and Graphics 4(3), 433–452 (2004)CrossRefGoogle Scholar
  6. 6.
    Chan, T.F., Vese, L.A.: Active Contours Without Edges. IEEE Transactions on Image Processing 7, 266–277 (2001)CrossRefGoogle Scholar
  7. 7.
    Lee, S.H., Seo, J.K.: Level Set-Based Bimodal Segmentation With Stationary Global Minimum. IEEE Transactions on Image Processing 15(9), 2843–2852 (2006)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  9. 9.
    Paclic, P., Duin, R., Kempen, G.V., Kohlus, R.: Supervised Segmentation of Textures in Backscatter Images. In: Proceedings of IEEE International Conference on Pattern Recognition, vol. 2, pp. 490–493 (2002)Google Scholar
  10. 10.
    Iakovidis, D.K., Maroulis, D.E, Karkanis, S.A.: A Comparative Study of Color-Texture Image Features. In: Proceedings of IEEE International Workshop on Systems, Signal and Image Processing, Halkida, Greece, pp. 205–209 (2005)Google Scholar
  11. 11.
    Osher, S., Sethian, J.: Fronts Propagating with Curvature- Dependent Speed: Algorithms Based on the Hamilton-Jacobi Formulations. Journal Of Computational Physics 79, 12–49 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Unser, M., Eden, M.: Nonlinear Operators for Improving Texture Segmentation Based on Features Extracted by Spatial Filtering. IEEE Transactions on Systems, Man and Cybernetics 20(4), 804–815 (1990)CrossRefGoogle Scholar
  13. 13.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michalis A. Savelonas
    • 1
  • Dimitris K. Iakovidis
    • 1
  • Dimitris Maroulis
    • 1
  1. 1.Dept. of Informatics and Telecommunications, University of Athens, Panepistimioupolis, 15784, Athens, Email: rtsimage@di.uoa.grGreece

Personalised recommendations