Pottslab - Multilabel segmentation of color images

Multilabel image segmentation for vector-valued images based on the Potts model
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Aggiornato 30 nov 2023

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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

Cita come

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

Compatibilità della release di MATLAB
Creato con R2016a
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Versione Pubblicato Note della release
1.0.0.3

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1.0.0.0

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