Geodesic active regions for supervised texture segmentation
- 1 January 1999
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 926-932 vol.2
- https://doi.org/10.1109/iccv.1999.790347
Abstract
The 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.Keywords
This publication has 19 references indexed in Scilit:
- Geodesic active contours for supervised texture segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Unifying boundary and region-based information for geodesic active trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- A computational approach to boundary detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Geodesic Active ContoursInternational Journal of Computer Vision, 1997
- Deformable boundary finding in medical images by integrating gradient and region informationIEEE Transactions on Medical Imaging, 1996
- Shape modeling with front propagation: a level set approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1995
- Unsupervised texture segmentation using Markov random field modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- Image segmentation by unifying region and boundary informationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- Multiresolution Approximations and Wavelet Orthonormal Bases of L 2 (R)Transactions of the American Mathematical Society, 1989
- Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulationsJournal of Computational Physics, 1988