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Variational Methods for Multimodal Image Matching
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“General approaches address multimodal image registration by either performing image synthesis [22, 23] to change the problem to a uni-modal image registration task, or by proposing complex, hand-crafted [24, 25, 26, 27] or learned [28, 29, 30, 31, 32] multi-modal image similarity measures.”
From Quicksilver: Fast predictive image registration – A deep learning approach (2017) · cites this paper as [28]
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Metadata
- Authors
- Hermosillo, G., Chefd'Hotel, C., & Faugeras, O.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- ORCID
- Faugeras, Olivier
- Venue
- International Journal of Computer Vision
- Published
- 2002-12
- Volume
- 50
- Issue
- 3
- Pages
- 329-343
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1518961746
- Cited by
- 388
- References
- 71
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 5 · 2022: 4 · 2021: 12 · 2020: 8 · 2019: 12 · 2018: 7 · 2017: 12 · 2016: 8 · 2015: 16 · 2014: 28 · 2013: 27 · 2012: 30
- FWCI
- 19.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Funders
- National Science Foundation
- Institut national de recherche en informatique et en automatique (INRIA)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- multimodal image registration, non-rigid registration, variational methods, statistical similarity measures, optical flow, well-posedness
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