Contour and Region-Based Image Segmentation



One of the most complex tasks in computer vision is segmentation. Segmentation can be roughly defined as optimally segregating the foreground from the background, or by finding the optimal partition of the image into its constituent parts. Here optimal segregation means that pixels (or blocks in the case of textures) in the foreground region share common statistics. These statistics should be significantly different from those corresponding to the background. In this context, active polygons models provide a discriminative mechanism for the segregation task. We will show that Jensen–Shannon (JS) divergence can efficiently drive such mechanism. Also, the maximum entropy (ME) principle is involved in the estimation of the intensity distribution of the foreground.

It is desirable that the segmentation process achieves good results (compared to the ones obtained by humans) without any supervision. However, such unsupervision only works in limited settings. For instance, in medical image segmentation, it is possible to find the contour that separates a given organ in which the physician is interested. This can be done with a low degree of supervision if one exploits the IT principle of minimum description length (MDL). It is then possible to find the best contour, both in terms of organ fitting and minimal contour complexity. IT inspires methods for finding the best contour both in terms of segregation and minimal complexity (the minimum description length principle).


Maximum Entropy Active Contour Minimum Description Length Hough Transformation Maximum Entropy Principle 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Key References

  1. M. Figueiredo, J. Leita~o, and A.K. Jain. “Unsupervised Contour Representation and Estimation Using B-splines and a Minimum Description Length Criterion”. IEEE Transactions on Image Processing 9(6): 1075–1087 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  2. G. Unal, A. Yezzi, and H. Krim. “Information-Theoretic Active Polygons for Unsupervised Texture Segmentation”. International Journal of Computer Vision 62(3):199–220 (2005)CrossRefGoogle Scholar
  3. G. Unal, H. Krim, and A. Yezzi. “Fast Incorporation of Optical Flow into Active Polygons”. IEEE Transactions on Image Processing 14(6): 745–759 (2005)CrossRefGoogle Scholar
  4. B. Jedynak, H. Zheng, and M. Daoudi. “Skin Detection Using Pairwise Models”. Image and Vision Computing 23(13): 1122–1130 (2005)CrossRefGoogle Scholar
  5. Z.W. Tu and S.C. Zhu. “Image Segmentation by Data-Driven Markov Chain Monte Carlo”. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5): 657–673 (2002)CrossRefGoogle Scholar
  6. Z.W. Tu, X.R. Chen, A.L. Yuille, and S.C. Zhu. “Image parsing: Unifying Segmentation, Detection and Recognition”. International Journal of Computer Vision 63(2):113–140 (2005)CrossRefGoogle Scholar
  7. S.C. Zhu and A.L. Yuille. “Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(9): 884–900, (1996)CrossRefGoogle Scholar
  8. S. Geman and D. Geman. “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6 (pp. 721–741), (1984).zbMATHCrossRefGoogle Scholar
  9. X. Chen and A.L. Yuille. “Time-Efficient Cascade for Real Time Object Detection”. First International Workshop on Computer Vision Applications for the Visually Impaired. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA (2004)Google Scholar
  10. G. Winkler. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction. Springer, New York (2003)Google Scholar

Copyright information

© Springer Verlag London Limited 2009

Personalised recommendations