Geodesic Active Regions for Supervised Texture Segmentation

  • Authors:
  • Nikos Paragios;Rachid Deriche

  • Affiliations:
  • -;-

  • Venue:
  • ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
  • Year:
  • 1999

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Abstract

This paper presents a novel variational method for supervised texture segmentation. 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.