Graph Cuts and Efficient N-D Image Segmentation
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Graph cut algorithms facilitate a global optimal solution.
“On the other hand, graph cut algorithms facilitate a global optimal solution [86].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [86]
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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 · see citing works
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- Authors
- Boykov, Y., & Funka-Lea, G.
- Affiliations
- Western University (CA)
- Siemens (United States) (US)
- Corresponding author
- Boykov, Yuri
- ORCID
- Boykov, Yuri
- Venue
- International Journal of Computer Vision
- Published
- 2006-11
- Volume
- 70
- Issue
- 2
- Pages
- 109-131
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2119300483
- Cited by
- 1,910
- References
- 1
- Citations by year
- 2026: 22 · 2025: 38 · 2024: 47 · 2023: 58 · 2022: 60 · 2021: 74 · 2020: 91 · 2019: 86 · 2018: 92 · 2017: 140 · 2016: 143 · 2015: 148 · 2014: 158 · 2013: 173 · 2012: 142
- FWCI
- 70.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- graph cuts, image segmentation, combinatorial optimization, object extraction, Mumford-Shah functional, boundary regularization, multi-scale segmentation, hierarchical segmentation, medical image segmentation, video segmentation
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