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Deformable-Model Based Textured Object Segmentation

  • Xiaolei Huang
  • Zhen Qian
  • Rui Huang
  • Dimitris Metaxas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)

Abstract

In this paper, we present a deformable-model based solution for segmenting objects with complex texture patterns of all scales. The external image forces in traditional deformable models come primarily from edges or gradient information and it becomes problematic when the object surfaces have complex large-scale texture patterns that generate many local edges within a same region. We introduce a new textured object segmentation algorithm that has both the robustness of model-based approaches and the ability to deal with non-uniform textures of both small and large scales. The main contributions include an information-theoretical approach for computing the natural scale of a “texon” based on model-interior texture, a nonparametric texture statistics comparison technique and the determination of object belongingness through belief propagation. Another important property of the proposed algorithm is in that the texture statistics of an object of interest are learned online from evolving model interiors, requiring no other a priori information. We demonstrate the potential of this model-based framework for texture learning and segmentation using both natural and medical images with various textures of all scales and patterns.

Keywords

Markov Random Field Implicit Representation Deformable Model Active Contour Model Texture Region 
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.

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References

  1. 1.
    Ali, S.M., Silvey, S.D.: A general class of coefficients of divergence of one distribution from another. J. Roy. Stat. Soc. 28, 131–142 (1966)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Aujol, J.F., Aubert, G., Blanc-Feraud, L.: Wavelet-based level set evolution for classification of textured images. IEEE Trans. on Image Processing 12(12), 1634–1641 (2003)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. In: IEEE Int’l Conf. on Computer Vision, pp. 694–699 (1995)Google Scholar
  4. 4.
    Chernoff, H.: Large-sample theory: Parametric case. Ann. Math. Stat. 27, 1–22 (1956)zbMATHMathSciNetCrossRefGoogle Scholar
  5. 5.
    Cohen, D.: On active contour models and balloons. CVGIP: Image Understanding 53, 211–218 (1991)zbMATHCrossRefGoogle Scholar
  6. 6.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Proc. of European Conf. on Computer Vision, vol. 2, pp. 484–498 (1998)Google Scholar
  7. 7.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  8. 8.
    Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A 2(7), 1160–1169 (1985)CrossRefGoogle Scholar
  9. 9.
    Huang, X., Metaxas, D., Chen, T.: Metamorphs: Deformable shape and texture models. In: IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 496–503 (2004)Google Scholar
  10. 10.
    Julesz, B.: Texons, the elements of texture perception, and their interactions. Nature 290(5802), 91–97 (1981)CrossRefGoogle Scholar
  11. 11.
    Kailath, T.: The divergence and bhattacharyya distance measures in signal selection. IEEE Trans. on Comm. Tech. 15(1), 52–60 (1967)CrossRefGoogle Scholar
  12. 12.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int’l Journal of Computer Vision 1, 321–331 (1987)CrossRefGoogle Scholar
  13. 13.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int’l Journal of Computer Vision 43(1), 7–27 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(2), 158–175 (1995)CrossRefGoogle Scholar
  15. 15.
    Manjunath, B., Chellapa, R.: Unsupervised texture segmentation using markov random field models. IEEE Trans. on Pattern Analysis and Machine Intelligence 13, 478–482 (1991)CrossRefGoogle Scholar
  16. 16.
    Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: Algorithms based on the Hamilton-Jacobi formulation. Journal of Computational Physics 79, 12–49 (1988)zbMATHMathSciNetCrossRefGoogle Scholar
  17. 17.
    Paragios, N., Deriche, R.: Geodesic active regions for supervised texture segmentation. In: IEEE Int’l Conf. on Computer Vision, pp. 926–932 (1999)Google Scholar
  18. 18.
    Paragios, N., Rousson, M., Ramesh, V.: Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration. In: European Conf. on Computer Vision, pp. II: 775–790 (2002)Google Scholar
  19. 19.
    Pearl, J.: Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman Publishers, San Francisco (1988)Google Scholar
  20. 20.
    Pujol, O., Radeva, P.: Texture segmentation by statistical deformable models. Int’l Journal of Image and Graphics 4(3), 433–452 (2004)CrossRefGoogle Scholar
  21. 21.
    Randen, T., Husoy, J.H.: Texture segmentation using filters with optimized energy separation. IEEE Trans. on Image Processing 8(4), 571–582 (1999)CrossRefGoogle Scholar
  22. 22.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics (SIGGRAPH) 23(3), 309–314 (2004)CrossRefGoogle Scholar
  23. 23.
    Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. In: Proceedings of the 13th Annual Conference on Computer Graphics, pp. 151–160 (1986)Google Scholar
  24. 24.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  25. 25.
    Staib, L.H., Duncan, J.S.: Boundary finding with parametrically deformable models. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(11), 1061–1075 (1992)CrossRefGoogle Scholar
  26. 26.
    Xu, C., Prince, J.L.: Generalized gradient vector flow external forces for active contours. Signal Processing 71, 131–139 (1998)zbMATHCrossRefGoogle Scholar
  27. 27.
    Zhu, S., Yuille, A.: Region Competition: Unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 18(9), 884–900 (1996)CrossRefGoogle Scholar
  28. 28.
    Zhu, S.C., Guo, C.E., Wang, Y.Z., Xu, Z.J.: What are textons? Int’l Journal of Computer Vision 62(1), 121–143 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xiaolei Huang
    • 1
  • Zhen Qian
    • 2
  • Rui Huang
    • 1
  • Dimitris Metaxas
    • 1
    • 2
  1. 1.Division of Computer and Information SciencesRutgers UniversityUSA
  2. 2.Department of Biomedical EngineeringRutgers UniversityUSA

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