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International Journal of Computer Vision

, Volume 46, Issue 3, pp 223–247 | Cite as

Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

  • Nikos Paragios
  • Rachid Deriche
Article

Abstract

This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.

supervised texture segmentation Gabor filters mixture analysis Geodesic Active Contours propagation of curves level set methods 

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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Nikos Paragios
    • 1
  • Rachid Deriche
    • 2
  1. 1.Imaging and Visualization DepartmentSiemens Corporate ResearchPrincetonUSA
  2. 2.Computer Vision and Robotics Group (RobotVis), I.N.R.I.ASophia AntipolisFrance

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