Pottslab - Multilabel segmentation of color images
Multilabel image segmentation for vector-valued images based on the Potts model
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Multilabel image segmentation for vector-valued images based on the Potts model (also called piecewise constant Mumford-Shah model)
Features:
- Multilabel image segmentation (2D domain) or step detection for signals (1D domain)
- No label discretization required (labels are chosen automatically)
- Supports multichannel images (e.g. RGB, multispectral or feature images) and has linear complexity in number of channels
- Supports indirect measurements by a linear operator (e.g. blurred data, computed tomography data)
- Supports multithreading
Cite As
M. Storath, A. Weinmann. "Fast partitioning of vector-valued images" SIAM Journal on Imaging Sciences, 2014
M. Storath, A. Weinmann, L. Demaret. "Jump-sparse and sparse recovery using Potts functionals" IEEE Transactions on Signal Processing, 2014
A. Weinmann, M. Storath, L. Demaret. "The L1-Potts functional for robust jump-sparse reconstruction" SIAM Journal on Numerical Analysis, 2015
A. Weinmann, M. Storath. "Iterative Potts and Blake-Zisserman minimization for the recovery of functions with discontinuities from indirect measurements." Proceedings of The Royal Society A, 471(2176), 2015
M. Storath, A. Weinmann, J. Frikel, M. Unser. "Joint image reconstruction and segmentation using the Potts model" Inverse Problems, 2015
General Information
- Version 1.0.0.3 (6.37 MB)
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View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.3 | Updated description |
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| 1.0.0.2 | Updated references |
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| 1.0.0.1 | Updated citations |
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| 1.0.0.0 | - description
- description
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