Name
Abstract | ||
|---|---|---|
We present a method for the unsupervised segmentation of textured images using Potts functionals, which are a piecewise-constant variant of the Mumford and Shah functionals. We propose a minimization strategy based on the alternating direction method of multipliers and dynamic programming. The strategy allows us to process large feature spaces because the computational cost grows only linearly in the feature dimension. In particular, our algorithm has more favorable computational costs for high-dimensional data than graph cuts. Our feature vectors are based on monogenic curvelets. They incorporate multiple resolutions and directional information. The advantage over classical curvelets is that they yield smoother amplitudes due to the envelope effect of the monogenic signal. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025883 | Image Processing |
Keywords | Field | DocType |
Potts model,dynamic programming,image resolution,image segmentation,image texture,minimisation,piecewise constant techniques,Mumford functionals,Potts functionals,Potts model,Shah functionals,directional information,dynamic programming,feature spaces,feature vectors,high-dimensional data,minimization strategy,monogenic curvelets,monogenic signal,multipliers,piecewise-constant variant,textured images,unsupervised texture segmentation,Potts functional,Texture segmentation,monogenic curvelets,piecewise constant Mumford and Shah functional | Cut,Dynamic programming,Computer vision,Feature vector,Pattern recognition,Segmentation,Computer science,Minification,Artificial intelligence,Potts model,Curvelet,Feature Dimension | Conference |
ISSN | Citations | PageRank |
1522-4880 | 2 | 0.37 |
References | Authors | |
20 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Storath | 1 | 138 | 12.69 |
| andreas weinmann | 2 | 138 | 12.81 |
| M Unser | 3 | 4335 | 499.89 |