Using sequential context for image analysis

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2010, Pages 2800-2803

  Paiva, António R C ^a   Jurrus, Elizabeth ^a,b   Tasdizen, Tolga ^a,b  

^a UNIVERSITY OF UTAH   (United States)

^b Department of Electrical and Computer Engineering   (United States)

Author keywords

Conditional random fields; Markov random fields; Neural networks; Sequential context inference

Indexed keywords

CONDITIONAL PROBABILITIES; CONDITIONAL RANDOM FIELD; CONTEXT INFERENCE; FAST INFERENCE; INPUT IMAGE; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; MODELING ARCHITECTURE;

IMAGE ANALYSIS; IMAGE SEGMENTATION; INFERENCE ENGINES; NETWORK ARCHITECTURE; PATTERN RECOGNITION;

NEURAL NETWORKS;

EID: 78149477500     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2010.686     Document Type: Conference Paper

References (13)

1. S. Z. Li, Markov Random Field Modeling in Image Analysis. Springer, 2001.
2. J. Pearl, Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco, CA: Morgan Kaufmann, 1988.
3. S. Z. Li, H. Wang, and K. L. Chan, "Minimization of MRF energy with relaxation labeling," Journal of Mathematical Imaging and Vision, vol. 7, no. 2, pp. 149-161, Mar. 1997.
4. J. Besag, "On the statistical analysis of dirty pictures," Journal of the Royal Statistical Society - Series B, vol. 48, no. 3, pp. 259-302, 1986.
5. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222-2139, Nov. 2001.
6. V. Kolmogorov and R. Zabih, "What energy functions can be minimized via graph cuts?" IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 147-159, Feb. 2004.
7. K. P. Murphy, Y. Weiss, and M. I. Jordan, "Loopy belief propagation for approximate inference: an empirical study," in Proceedings of the Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, 1999, pp. 467-475.
8. Z. Kou and W. W. Cohen, "Stacked graphical models for efficient inference in Markov random fields," in Proceedings of the SIAM International Conference on Data Mining, Minneapolis, Minnesota, Apr. 2007.
9. Z. Tu, "Auto-context and its application to high-level vision tasks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, Jun. 2008.
10. S. Haykin, Neural networks - A comprehensive foundation, 2nd ed. Prentice-Hall, 1999.
11. K. Hornik, "Approximation capabilities of multilayer feedforward networks," Neural Networks, vol. 4, no. 2, pp. 251-257, 1992.
12. G. Cybenko, "Approximation by superpositions of a sigmoidal function," Mathematics of Control, Signals, and Systems, vol. 2, no. 4, pp. 303-314, Dec. 1989.
13. E. Borenstein, E. Sharon, and S. Ullman, "Combining top-down and bottom-up segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, D.C., USA, Jun. 2004.