The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
Abstract
This paper presents a new approach to the use of Gibbs distributions (GD) for modeling and segmentation of noisy and textured images. Specifically, the paper presents random field models for noisy and textured image data based upon a hierarchy of GD. It then presents dynamic programming based segmentation algorithms for noisy and textured images, considering a statistical maximum a posteriori (MAP) criterion. Due to computational concerns, however, sub-optimal versions of the algorithms are devised through simplifying approximations in the model. Since model parameters are needed for the segmentation algorithms, a new parameter estimation technique is developed for estimating the parameters in a GD. Finally, a number of examples are presented which show the usefulness of the Gibbsian model and the effectiveness of the segmentation algorithms and the parameter estimation procedures.
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Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Beitrag zur Theorie des Ferromagnetismus
Ising, E. (1925) · Zeitschrift für Physik · doi:10.1007/bf02980577 · Cited by 3,871
Cited by
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Constrained restoration and the recovery of discontinuities
Geman, D., & Reynolds, G. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.120331 · Cited by 1,085
Statistical approach to segmentation of single-channel cerebral MR images
Rajapakse, J. C., Giedd, J. N., & Rapoport, J. L. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563663 · Cited by 678
(1998) · Cited by 540
Approaches for automated detection and classification of masses in mammograms
Cheng, H. D., Shi, X. J., Min, R., Hu, L. M., Cai, X. P., & Du, H. N. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2005.07.006 · Cited by 538
Metadata
- Authors
- Derin, H., & Elliott, H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1987-01
- Volume
- PAMI-9
- Issue
- 1
- Pages
- 39-55
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W1989222601
- Cited by
- 977
- References
- 2
- FWCI
- 29.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- Derin, H., & Elliott, H. (1987). Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.1987.4767871
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
