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Unsupervised texture segmentation using monogenic curvelets and the Potts model.
In: Proceedings of the ICIP 2014 (IEEE International Conference on Image Processing (ICIP 2014), 27 - 30 October 2014, Paris, France). IEEE, 2014. 4348-4352
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.
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Publikationstyp Artikel: Konferenzbeitrag
Konferenztitel IEEE International Conference on Image Processing (ICIP 2014)
Konferzenzdatum 27 - 30 October 2014
Konferenzort Paris, France
Konferenzband Proceedings of the ICIP 2014
Quellenangaben Seiten: 4348-4352
Institut(e) Institute of Computational Biology (ICB)