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Geometric Level Set Methods in Imaging, Vision, and Graphics
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Cited by
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
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910
Ultrasound image segmentation: a survey
Noble, J. A., & Boukerroui, D. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.877092 · Cited by 1,157
Comparison and evaluation of methods for liver segmentation from CT datasets
Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
Li, K., Wu, X., Chen, D. Z., & Sonka, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.19 · Cited by 664
Recent progress in semantic image segmentation
Liu, X., Deng, Z., & Yang, Y. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-018-9641-3 · Cited by 455
Nguyen, T. T., Yvonnet, J., Zhu, Q.-Z., Bornert, M., & Chateau, C. (2016) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2015.10.007 · Cited by 359
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
Michailovich, O., Rathi, Y., & Tannenbaum, A. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.908073 · Cited by 320
From Gestalt Theory to Image Analysis: A Probabilistic Approach
Desolneux, A., Moisan, L., & Morel, J.-M. (2008) · Interdisciplinary Applied Mathematics · doi:10.1007/978-0-387-74378-3 · Cited by 300
The Flexible, Extensible and Efficient Toolbox of Level Set Methods
Mitchell, I. M. (2008) · Journal of Scientific Computing · doi:10.1007/s10915-007-9174-4 · Cited by 253
Generalized Gradients: Priors on Minimization Flows
Charpiat, G., Maurel, P., Pons, J.-P., Keriven, R., & Faugeras, O. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-9966-2 · Cited by 91
Metadata
- Authors
- Osher, S., & Paragios, N.
- Venue
- Springer eBooks
- Publisher
- Springer New York
- Published
- 2003
- OpenAlex
- W1604679511
- Cited by
- 799
- References
- 0
- FWCI
- 30.12
- Type
- book
- Language
- en
- Open access
- closed
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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.
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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.