An Algorithm for the Detection and Measurement of Rail Surface Defects

References

• Alfelor , R. 1991 . Analysis of Automatically Collected Rail Surface Defect Data for Rail Maintenance Management , Ph.D. dissertation Carnegie Mellon University, Dept. of Civil Engineering .
   Google Scholar
• Amit , Y. , Grenander , U. and Piccioni , M. 1991 . Structural Image Restoration Through Deformable Templates . Journal of the American Statistical Association , 86 : 376 – 387 .
   Web of Science ®Google Scholar
• Besag , J. 1974 . Spatial Interaction and the Statistical Analysis of Lattice Systems . Journal of the Royal Statistical Society , 36 : 192 – 236 . Ser. B
   Google Scholar
• Besag , J. 1986 . On the Statistical Analysis of Dirty Pictures . Journal of the Royal Statistical Society , 48 : 259 – 302 . Ser. B
   Google Scholar
• Cohen , F. S. , Cooper , D. B. , Silverman , J. F. and Hinkle , E. B. “Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Textured Images Based on Noncausal Markovian Random Field Models,” . Proceedings of the Seventh International Conference on Pattern Recognition . pp. 1104 – 1107 .
   Google Scholar
• Cressie , N. and Lele , S. 1990 . New Models for Markov Random Fields , Iowa State University, Dept. of Statistics . Preprint Number 90–26
   Google Scholar
• Derin , H. and Elliott , H. 1987 . Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields . IEEE Transactions on Pattern Analysis and Machine Intelligence , 9 : 39 – 55 .
   PubMed Web of Science ®Google Scholar
• Derin , H. , Elliott , H. , Cristi , R. and Geman , D. 1984 . Bayes Smoothing Algorithms for Segmentation of Binary Images Modeled by Markov Random Fields . IEEE Transactions on Pattern Analysis and Machine Intelligence , 6 : 707 – 720 .
   PubMed Web of Science ®Google Scholar
• Geman , D. and Geman , S. 1986 . “ Bayesian Image Analysis ” . In Disordered Systems and Biological Organization , Edited by: Bienenstock , E. , Fogelman , F. and Welsbuch , G. 301 – 319 . Berlin : Springer-Verlag .
   Google Scholar
• Geman , D. , Geman , S. and Graffigne , C. 1987 . “ Locating Texture and Object Boundaries ” . In Pattern Recognition Theory and Applications , Edited by: Devijver , P. A. and Kittler , J. 165 – 177 . Berlin : Springer-Verlag .
   Google Scholar
• Geman , D. , Geman , S. , Graffigne , C. and Dong , P. 1990 . Boundary Detection by Constrained Optimization . IEEE Transactions on Pattern Analysis and Machine Intelligence , 12 : 609 – 628 .
   Web of Science ®Google Scholar
• Geman , S. and Geman , D. 1984 . Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images . IEEE Transactions on Pattern Analysis and Machine Intelligence , 6 : 721 – 741 .
   PubMed Web of Science ®Google Scholar
• Huang , J. S. and Tseng , D. H. 1988 . Statistical Theory of Edge Detection . Computer Vision, Graphics, and Image Processing , 43 : 337 – 346 .
   Google Scholar
• Johnson , V. 1989 . On Statistical Image Reconstruction , Ph.D. dissertation University of Chicago, Dept. of Statistics .
   Google Scholar
• Kittler , J. and Föglein . “Contextual Decision Rules for Objects in Lattice Configurations,” . Proceedings of the Seventh International Conference on Pattern Recognition . pp. 1104 – 1107 .
   Google Scholar
• Kittler , J. and Pairman , D. 1985 . Contextual Pattern Recognition Applied to Cloud Detection and Identification . IEEE Transactions of Geoscience and Remote Sensing , 23 : 855 – 863 .
   Web of Science ®Google Scholar
• Klein , R. and Press , S. J. 1990 . “ Bayesian Contextual Classification With Neighbors Correlated with Training Data ” . In Bayesian and Likelihood Methods in Statistics and Econometrics , Edited by: Geisser , S. , Hodges , J. S. , Press , S. J. and Zellner , A. 337 – 355 . New York : Elsevier .
   Google Scholar
• Short , T. H. 1991 . Algorithms for Simultaneous Image Restoration and Segmentation , Ph.D. dissertation Carnegie Mellon University, Dept. of Statistics .
   Google Scholar
• Tanaka , M. and Katayama , T. 1989 . Edge Detection and Restoration of Noisy Images by the Expectation-Maximization Algorithm . Signal Processing , 17 : 213 – 226 .
   Google Scholar
• Tsay , R. S. 1988 . Statistical Image Reconstruction , Carnegie Mellon University, Dept. of Statistics . Technical Report No. 447
   Google Scholar