The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Digital Image Processing: Towards Bayesian image analysis
Abstract
Many of the tasks encountered in image processing can be considered as problems in statistical inference. In particular, they fit naturally into a subjectivist Bayesian framework. In this paper, we describe the Bayesian approach to image analysis. Numerical examples are not included but can be found among the references, in the previous Special Issue of this Journal and elsewhere. It is argued that the Bayesian approach, still in its infancy, has considerable potential for future development.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Statistical Analysis of Field Experiments Using Neighbouring Plots
Besag, J., & Kempton, R. (1986) · Biometrics · doi:10.2307/2531047 · Cited by 246
Self-citation
Cited by
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
Nonlinear image recovery with half-quadratic regularization
Geman, D., & Yang, C. (1995) · IEEE Transactions on Image Processing · doi:10.1109/83.392335 · Cited by 1,000
Initial in vivo experience with steady‐state subzone‐based MR elastography of the human breast
Van Houten, E. E. W., Doyley, M. M., Kennedy, F. E., Weaver, J. B., & Paulsen, K. D. (2003) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10232 · Cited by 224
Review of Postprocessing Techniques for Compression Artifact Removal
Shen, M.-Y., & Kuo, C.-C. J. (1998) · Journal of Visual Communication and Image Representation · doi:10.1006/jvci.1997.0378 · Cited by 175
Metadata
- Authors
- Besag, J.
- Venue
- Journal of Applied Statistics
- Publisher
- Informa UK Limited
- Published
- 1989-01
- Volume
- 16
- Issue
- 3
- Pages
- 395-407
- ISSN
- 0266-4763 (ISSN-L)
- 1360-0532
- OpenAlex
- W2320459610
- Cited by
- 226
- References
- 1
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Digital Image Processing: Towards Bayesian image analysis. https://app.cochat.ai/papers/10.1080/02664768900000049
- The paper
- Besag, J. (1989). Digital Image Processing: Towards Bayesian image analysis. Journal of Applied Statistics. https://doi.org/10.1080/02664768900000049
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.