Geodesic Active Regions for Supervised Texture Segmentation

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.

• Type: Conference
• Year: 1999
• Where: ICCV
• Authors: Nikos Paragios, Rachid Deriche
• Keywords: Computer Vision, ICCV 1999, Level Set, Supervised Texture Segmentation, Texture Segmentation