Advertisement

Parsing Images into Region and Curve Processes

  • Zhuowen Tu
  • Song-Chun Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)

Abstract

Natural scenes consist of a wide variety of stochastic patterns. While many patterns are represented well by statistical models in two dimensional regions as most image segmentation work assume, some other patterns are fundamentally one dimensional and thus cause major problems in segmentation. We call the former region processes and the latter curve processes. In this paper, we propose a stochastic algorithm for parsing an image into a number of region and curve processes. The paper makes the following contributions to the literature. Firstly, it presents a generative rope model for curve processes in the form of Hidden Markov Model (HMM). The hidden layer is a Markov chain with each element being an image base selected from an over-complete basis, such as Difference of Gaussians (DOG) or Difference of Offset Gaussians (DOOG) at various scales and orientations. The rope model accounts for the geometric smoothness and photometric coherence of the curve processes. Secondly, it integrates both 2D region models, such as textures, splines etc with 1D curve models under the Bayes framework. Because both region and curve models are generative, they compete to explain input images in a layered representation. Thirdly, it achieves global optimization by effective Markov chain Monte Carlo methods in the sense of maximizing a posterior probability. The Markov chain consists of reversible jumps and diffusions driven by bottom up information. The algorithm is applied to real images with satisfactory results. We verify the results through random synthesis and compare them against segmentations with region processes only.

Keywords

Markov Chain Hide Markov Model Curve Model Markov Chain Monte Carlo Method Reversible Jump 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. August and S.W. Zucker. A generative model of curve images with a completely-characterized non-gaussian joint distribution. In Proc. of Workshop on Stat. and Comp. Theories of Vis., July, 2001.Google Scholar
  2. 2.
    M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. Proc. ECCV, 1996.Google Scholar
  3. 3.
    T.F. Cootes, D. Cooper, C.J. Taylor and J. Graham. Active shape models-their training and application. Computer Vision and Image Understanding, vol. 61, no. 1, Jan. pages 38–59, 1995.CrossRefGoogle Scholar
  4. 4.
    U. Grenander and M. I. Miller. Representation of knowledge in complex systems. J. R. Stat. Soc., B, vol 56, 549–603, 1994zbMATHMathSciNetGoogle Scholar
  5. 5.
    P. J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, vol. 82, 711–732, 1995.zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    J. Malik, S. Belongie, T. Leung, and J. Shi. Contour and texture analysis for image segmentation. IJCV, vo. 43, no. 1, June 2001.Google Scholar
  7. 7.
    S.G. Mallat and Z. Zhang. Matching pursuits with time-frequency dictionaries. IEEE Tranc. on Signal Processing, vol. 41, no. 12, Dec. 1993.Google Scholar
  8. 8.
    D. Mumford. Algebraic geometry and its applications, chaper elastic and computer vision, pp. 491–506. Springer-Verlag, 1994.Google Scholar
  9. 9.
    B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: a strategy employed by v1?. Vision Res., vol. 37, no. 23 3311–25 1997.CrossRefGoogle Scholar
  10. 10.
    M. Kass, A. Witkin and D. Terzopoulos. Snakes: active contour models. IJCV, 1, 1988.Google Scholar
  11. 11.
    Z. Tu, S.C. Zhu., and H.Y. Shum. Image segmentation by data driven markov chain monte carlo. Proc. ICCV, 2001.Google Scholar
  12. 12.
    S. C. Zhu and A. L. Yuille. Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Trans. PAMI vol. 18, No. 9, 1996.Google Scholar
  13. 13.
    S.C. Zhu. Embedding gestalt laws in markov random fields-a theory for shape modeling and perceptual organization. PAMI, vol. 21, no. 11, Nov. 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Zhuowen Tu
    • 1
  • Song-Chun Zhu
    • 1
  1. 1.Department of Computer and Information ScienceThe Ohio State UniversityUSA

Personalised recommendations