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Unsupervised Texture Segmentation Using Monogenic Curvelets and the Potts Model

M. Storath, A. Weinmann, M. Unser

Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP'14), Paris, French Republic, October 27-30, 2014, pp. 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.

AUTHOR="Storath, M. and Weinmann, A. and Unser, M.",
TITLE="Unsupervised Texture Segmentation Using Monogenic Curvelets and
        the {P}otts Model",
BOOKTITLE="Proceedings of the 2014 {IEEE} International Conference on
        Image Processing ({ICIP'14})",
address="Paris, French Republic",
month="October 27-30,",

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