A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting
Open accessAbstract
An interactive framework for soft segmentation and matting of natural images and videos is presented in this paper. The proposed technique is based on the optimal, linear time, computation of weighted geodesic distances to the user-provided scribbles, from which the whole data is automatically segmented. The weights are based on spatial and/or temporal gradients, without explicit optical flow or any advanced and often computationally expensive feature detectors. These could be naturally added to the proposed framework as well if desired, in the form of weights in the geodesic distances. A localized refinement step follows this fast segmentation in order to accurately compute the corresponding matte function. Additional constraints into the distance definition permit to efficiently handle occlusions such as people or objects crossing each other in a video sequence. The presentation of the framework is complemented with numerous and diverse examples, including extraction of moving foreground from dynamic background, and comparisons with the recent literature.
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The geodesic distance transform encodes spatial regularization and contrast-sensitivity.
“the geodesic distance transform [35], [36], [41] encodes spatial regularization and contrast-sensitivity”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [35]
Most interactive image segmentation methods rely on low-level features and require a relatively large amount of user interactions to deal with images with low contrast and ambiguous boundaries.
“However, most of these methods rely on low-level features and require a relatively large amount of user interactions to deal with images with low contrast and ambiguous boundaries.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [35]
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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502 · see citing works
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GeoS: Geodesic Image Segmentation
Criminisi, A., Sharp, T., & Blake, A. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_9 · Cited by 277 · see citing works
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Exploring ELM-based spatial–spectral classification of hyperspectral images
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- Authors
- Bai, X., & Sapiro, G.
- Affiliations
- University of Minnesota (US)
- Venue
- 2007 IEEE 11th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2007
- Pages
- 1-8
- OpenAlex
- W2134839354
- Cited by
- 438
- References
- 24
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 12 · 2022: 11 · 2021: 11 · 2020: 15 · 2019: 17 · 2018: 23 · 2017: 21 · 2016: 37 · 2015: 32 · 2014: 40 · 2013: 43 · 2012: 39
- FWCI
- 24.08
- Type
- conference paper
- Language
- en
- Open access
- green
- purl.umn.edu
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- University of Minnesota Digital Conservancy (University of Minnesota) (submittedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- interactive segmentation, video segmentation, image matting, scribble-based segmentation, spatial and temporal gradients, occlusion handling, foreground segmentation
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