EPFL
 Biomedical Imaging GroupSTI
EPFL
  Publications
English only   BIG > Publications > Potts Segmentation


 CONTENTS
 Home Page
 News & Events
 People
 Publications
 Tutorials and Reviews
 Research
 Demos
 Download Algorithms

 DOWNLOAD
 PDF
 Postscript
 All BibTeX References

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.


@INPROCEEDINGS(http://bigwww.epfl.ch/publications/storath1403.html,
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})",
YEAR="2014",
editor="",
volume="",
series="",
pages="4348--4352",
address="Paris, French Republic",
month="October 27-30,",
organization="",
publisher="",
note="")

© 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.