Minimal Partition (Chan-Vese) with Minimal Proximal splitting scheme. Application to image segmentation and depth map disparity.
- author: Nelly Pustelnik (nelly.pustelnik@ens-lyon.fr) CNRS, laboratoire de Physique de l'ENS de Lyon
- Contributor : Laurent Condat (laurent.condat@gipsa-lab.grenoble-inp.fr)
- date: Saturday, October 30 2010
- License CeCILL-B
- RECOMMENDATIONS:
- This toolbox is designed to work with Matlab 2017.b
DESCRIPTION: Proximal splitting algorithms for convex optimization are largely used in signal and image processing. They make possible to call the individual proximity operators of an arbitrary number of functions, whose sum is to be minimized. But the larger this number, the slower the convergence. In this work, we show how to compute the proximity operator of a sum of two functions, for a certain type of functions operating on objects having a graph structure. The gain provided by avoiding unnecessary splitting is illustrated by an application to image segmentation and depth map estimation.
HOW TO RUN:
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compile the mex files : cd include/ mex proj_decreasing_mex.c
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run the demo files :
- segmentation: "demo_segmentation.m" or "demo_segmentation_comp.m"
- disparity map: "demo_disparitymap.m" or "demo_disparitymap_comp.m"
The main function is "algo_MPMS".
RELATED PUBLICATION:
N. Pustelnik, L. Condat, Proximity operator of a sum of functions; Application to depth map estimation, IEEE Signal Processing Letters, vol. 24, no. 12, Dec. 2017