The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Learning Conditional Random Fields for Stereo
Abstract
State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose heuristic restrictions or priors on disparities, for example by modulating local smoothness costs with intensity gradients. In this paper we seek to replace such heuristics with explicit probabilistic models of disparities and intensities learned from real images. We have constructed a large number of stereo datasets with ground-truth disparities, and we use a subset of these datasets to learn the parameters of conditional random fields (CRFs). We present experimental results illustrating the potential of our approach for automatically learning the parameters of models with richer structure than standard hand-tuned MRF models.
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Cites
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
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
Self-citationFactor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486 · cited 1×
“Figure 1 illustrates the factorization we use for our models using a factor graph [30].”
MethodAn Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
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
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
(2001) · Cited by 1,932
Collins, M. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118694 · Cited by 1,862
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DehazeNet: An End-to-End System for Single Image Haze Removal
Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2598681 · Cited by 3,367
AOD-Net: All-in-One Dehazing Network
Li, B., Peng, X., Wang, Z., Xu, J., & Feng, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.511 · Cited by 2,342
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618
Self-citationBOP: Benchmark for 6D Object Pose Estimation
Hodaň, T., Michel, F., Brachmann, E., Kehl, W., Buch, A. G., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., et al. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_2 · Cited by 470
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
- Scharstein, D., & Pal, C.
- Venue
- 2007 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2007-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2099712288
- Cited by
- 851
- References
- 55
- FWCI
- 16.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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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.
