Advertisement

Patch-Based Texture Edges and Segmentation

  • Lior Wolf
  • Xiaolei Huang
  • Ian Martin
  • Dimitris Metaxas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)

Abstract

A novel technique for extracting texture edges is introduced. It is based on the combination of two ideas: the patch-based approach, and non-parametric tests of distributions.

Our method can reliably detect texture edges using only local information. Therefore, it can be computed as a preprocessing step prior to segmentation, and can be very easily combined with parametric deformable models. These models furnish our system with smooth boundaries and globally salient structures.

Keywords

Deformable Model Active Contour Model Texture Synthesis Texture Segmentation Free Form Deformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full conference paper text

References

  1. 1.
    Amini, A.A., Chen, Y., Elayyadi, M., Radeva, P.: Tag surface reconstruction and tracking of myocardial beads from SPAMM-MRI with parametric b-spline surfaces. IEEE Transactions on Medical Imaging 20(2) (2001)Google Scholar
  2. 2.
    Awate, S., Whitaker, R.: Image Denoising with Unsupervised, Information-Theoretic, Adaptive Filtering. University of Utah TR, UUCS-04-013 (2004)Google Scholar
  3. 3.
    Bardinet, E., Cohen, L.D., Ayache, N.: A parametric deformable model to fit unstructured 3D data. Computer Vision and Image Understanding 71(1) (1998)Google Scholar
  4. 4.
    Bouix, S., Siddiqi, K., Tannenbaum, A.: Flux Driven Automatic Centerline Extraction. Medical Image Analysis (2004)Google Scholar
  5. 5.
    Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 109–122. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover (1966)Google Scholar
  7. 7.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. In: ICCV (1995)Google Scholar
  8. 8.
    Drori, I., Cohen-Or, D., Yeshurun, H.: Fragment-based image completion. In: SIGGRAPH (2003)Google Scholar
  9. 9.
    Efros, A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: SIGGRAPH (2001)Google Scholar
  10. 10.
    Fogel, I., Sagi, D.: Gabor Filters as Texture Discriminator. Bio.Cybernetics (1989)Google Scholar
  11. 11.
    Fowlkes, C., Martin, D., Malik, J.: Learning Affinity Functions for Image Segmentation: Combining Patch-based and Gradient-based Approaches. In: CVPR (2003)Google Scholar
  12. 12.
    Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. IJCV 40(1) (2000)Google Scholar
  13. 13.
    Freeman, W.T., Jones, T.R., Pasztor, E.: Example-based super-resolution. IEEE Computer Graphics and Applications (2002)Google Scholar
  14. 14.
    Galun, M., Sharon, E., Basri, R., Brandt, A.: Texture Segmentation by Multiscale Aggregation of Filter Responses and Shape Elements. In: ICCV, pp. 716–723 (2003)Google Scholar
  15. 15.
    Heeger, D.J., Bergen, J.R.: Pyramid-based texture analysis/synthesis. In: SIGGRAPH (1995)Google Scholar
  16. 16.
    Hofmann, T., Puzicha, J., Buhmann, J.M.: Unsupervised Segmentation of Textured Images by Pairwise Data Clustering. In: ICIP (1996)Google Scholar
  17. 17.
    Hofmann, T., Puzicha, J., Buhmann, J.M.: An Optimization Approach to Unsupervised Hierarchical Texture Segmentation. In: ICIP (1997)Google Scholar
  18. 18.
    Hofmann, T., Puzicha, J., Buhmann, J.M.: Unsupervised Texture Segmentation in a Deterministic Annealing Framework. PAMI (1998)Google Scholar
  19. 19.
    Huang, X., Metaxas, D., Chen, T.: Metamorphs: Deformable Shape and Texture Models. In: CVPR (2004)Google Scholar
  20. 20.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1 (1987)Google Scholar
  21. 21.
    Liang, L., Liu, C., Xu, Y., Guo, B., Shum, H.Y.: Real-time texture synthesis by patch-based sampling. MSR-TR-2001-40,Microsoft Research (2001)Google Scholar
  22. 22.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modelling with front propagation: a level set approach. TPAMI 17(2), 158–175 (1995)CrossRefGoogle Scholar
  23. 23.
    Malik, J., Perona, P.: Preattentive Texture Discrimination with Early Vision Mechanisms. J. Optical Soc. Am. 7(2), 923–932 (1990)CrossRefGoogle Scholar
  24. 24.
    Martin, D., Fowlkes, C., Malik, J.: Learning to Detect Natural Image Boundaries Using Local Brightness. Color and Texture Cues, TPAMI 26(5) (2004)Google Scholar
  25. 25.
    Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: Algorithms based on the Hamilton-Jacobi formulation. J. of Comp. Physics (1988)Google Scholar
  26. 26.
    Otsu, N.: A threshold selection method from gray level histograms. IEEE. Trans. Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  27. 27.
    Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. In: IJCV (2002)Google Scholar
  28. 28.
    Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. IJCV 40(1) (2000)Google Scholar
  29. 29.
    Rayner, J.C.W., Best, D.J.: A Contingency Table Approach to Nonparametric Testing. Chapman & Hall/CRC (2001)Google Scholar
  30. 30.
    Rousson, M., Deriche, R.: A variational framework for active and adaptive segmentation of vector valued images. Motion and Video Computing (2002)Google Scholar
  31. 31.
    Rousson, M., Brox, T., Deriche, R.: Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space. In: Proc. of CVPR, vol. 2, pp. 699–704 (2003)Google Scholar
  32. 32.
    Rubner, Y., Tomasi, C.: Coalescing Texture Descriptors. In: Proc. of the ARPA Image Understanding Workshop (1996)Google Scholar
  33. 33.
    Sandberg, B., Chan, T., Vese, L.: A level-set and Gabor based active contour algorithm for segmenting textured images. UCLA Math. Report, 02-39 (July 2002)Google Scholar
  34. 34.
    Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. In: Proc. Annual Conference on Computer Graphics (1986)Google Scholar
  35. 35.
    Staib, L.H., Duncan, J.S.: Boundary finding with parametrically deformable models. TPAMI 14(11) (1992)Google Scholar
  36. 36.
    Ullman, S., Sali, E.: Object classification using a fragment-based representation. In: Bülthoff, H.H., Poggio, T.A., Lee, S.-W. (eds.) BMCV 2000. LNCS, vol. 1811, pp. 73–87. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  37. 37.
    Voorhees, H., Poggio, T.: Computing Texture Boundaries from Images. Nature 333, 364–367 (1988)CrossRefGoogle Scholar
  38. 38.
    Wilcoxon, F.: Individual Comparisons by Ranking Methods. Biometrics 1 (1945)Google Scholar
  39. 39.
    Witkin, A.W.: Intensity-based Edge Classification. In: Proc. AAAI (1982)Google Scholar
  40. 40.
    Leclerc, Y.: Computing the Local Structure in Two Dimensions. Readings in Computer Vision: issues, problems, principles and paradigms (1987)Google Scholar
  41. 41.
    Zhu, S.C., Yuille, A.: Region Competition: Unifying snakes, region growing and Bayes/MDL for multiband image segmentation. PAMI 18, 884–900 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lior Wolf
    • 1
  • Xiaolei Huang
    • 2
  • Ian Martin
    • 1
  • Dimitris Metaxas
    • 2
  1. 1.Center for Biological and Computational Learning, The McGovern Institute for Brain Research and dept. of Brain & Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Division of Computer and Information SciencesRutgers UniversityNew BrunswickUSA

Personalised recommendations

Cite paper