A Fast Anisotropic Mumford-Shah Functional Based Segmentation
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Joint brain parametric T1-map segmentation and RF inhomogeneity calibration
Journal of Biomedical Imaging
A probabilistic shape filter for online contour tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multiphase joint segmentation-registration and object tracking for layered images
IEEE Transactions on Image Processing
Multi-threshold level set model for image segmentation
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
A Probabilistic Contour Observer for Online Visual Tracking
SIAM Journal on Imaging Sciences
Motion objects segmentation using a new level set based method
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering
3D Video Based Segmentation and Motion Estimation with Active Surface Evolution
Journal of Signal Processing Systems
Pattern Recognition and Image Analysis
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A geometric formulation of active contours for 2D, 3D boundary detection and motion tracking is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of minimal distance curves or minimal surfaces in a Riemannian space whose metric is derived from the image. Previous models of geometric active contours are improved allowing stable boundary detection when their gradients suffer from large variations, including gaps. Numerical experiments are also presented.