The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters
Abstract
Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use graph cuts or belief propagation for inference. These stereo algorithms differ in both the inference algorithm used and the formulation of the MRF. It is unknown whether to attribute the responsibility for differences in performance to the MRF or the inference algorithm. We address this through controlled experiments by comparing the belief propagation algorithm and the graph cuts algorithm on the same MRF's, which have been created for calculating stereo disparities. We find that the labellings produced by the two algorithms are comparable. The solutions produced by graph cuts have a lower energy than those produced with belief propagation, but this does not necessarily lead to increased performance relative to the ground truth.
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
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
Spatial Interaction and the Statistical Analysis of Lattice Systems
Besag, J. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00999.x · Cited by 6,067
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577
What energy functions can be minimized via graph cuts?
Kolmogorov, V., & Zabih, R. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262177 · Cited by 3,127
Freeman, W. T., Pasztor, E. C., & Carmichael, O. T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026501619075 · Cited by 1,459
Understanding belief propagation and its generalizations
Yedidia, J. S., Freeman, W. T., & Weiss, Y. (2003) · Cited by 1,288
Multi-camera Scene Reconstruction via Graph Cuts
Kolmogorov, V., & Zabih, R. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47977-5_6 · Cited by 739
A pixel dissimilarity measure that is insensitive to image sampling
Birchfield, S., & Tomasi, C. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.677269 · Cited by 585
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
Weiss, Y., & Freeman, W. T. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910585 · Cited by 581
Cited by
Learning Conditional Random Fields for Stereo
Scharstein, D., & Pal, C. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383191 · Cited by 851
Describing Clothing by Semantic Attributes
Chen, H., Gallagher, A., & Girod, B. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_44 · Cited by 411
A Comparative Study of Energy Minimization Methods for Markov Random Fields
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., & Rother, C. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744047_2 · Cited by 386
Ghamisi, P., Maggiori, E., Li, S., Souza, R., Tarablaka, Y., Moser, G., De Giorgi, A., Fang, L., Chen, Y., Chi, M., et al. (2018) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2018.2854840 · Cited by 325
Training an Active Random Field for Real-Time Image Denoising
Barbu, A. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2028254 · Cited by 105
Metadata
- Authors
- Tappen, & Freeman
- Venue
- Proceedings Ninth IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2003
- Pages
- 900-906 vol.2
- OpenAlex
- W2169282664
- Cited by
- 451
- References
- 16
- FWCI
- 14.81
- Type
- conference paper
- 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: Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters. https://app.cochat.ai/papers/10.1109/iccv.2003.1238444
- The paper
- Tappen, & Freeman. (2003). Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters. Proceedings Ninth IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.2003.1238444
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.
