Benchmarking segmentation results using a Markov model and a Bayes information criterion

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 4877, Issue , 2002, Pages 248-254

  Murtagh, Fionn ^a   Qiao, Xiaoyu ^a   Crookes, Danny ^a   Walsh, Paul ^a   Basheer, P A Muhammed ^a   Long, Adrian ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; MARKOV PROCESSES; VECTORS;

BAYES INFORMATION CRITERION;

IMAGE SEGMENTATION;

EID: 0041731699     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.467441     Document Type: Conference Paper

References (10)

1. J. Besag. Statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B, 48:259-302, 1986.
2. G. Cross and A. Jain. Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:25-39, 1983.
3. A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series, B, 39:1-22, 1977.
4. N. Fatemi-Ghomi. Performance Measures for Wavelet-Based Segmentation Algorithms. PhD thesis, Surrey University, 1997.
5. C. Ji and L. Seymour. A consistent model selection procedure for Markov random fields based on penalized pseudolikelihood. Journal of Applied Probability, 6:423-443, 1996.
6. R. Kass and A. Raftery. Bayes factors. Journal of the American Statistical Association, 90:773-795, 1995.
7. G. McLachlan and T. Krishnan. The EM Algorithm and Extensions. Wiley, 1997.
8. D.C. Stanford. Fast Automatic Unsupervised Image Segmentation and Curve Detection in Spatial Point Patterns. PhD thesis, University of Washington, 1999.
9. D.C. Stanford and A.E. Raftery. A consistency result for a penalized pseudolikelihood criterion for model selection with spatially dependent mixture models. Technical report, Department of Statistics, University of Washington, 1999.
10. D.C. Stanford and A.E. Raftery. Determining the number of colors or gray levels in an image using approximate bayes factors: the pseudolikelihood information criterion (PLIC). Technical report, Department of Statistics, University of Washington, 2001.