A Moving Grid Framework for Geometric Deformable Models
International Journal of Computer Vision
A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
SIAM Journal on Imaging Sciences
Topology noise removal for curve and surface evolution
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
A combined segmentation and registration framework with a nonlinear elasticity smoother
Computer Vision and Image Understanding
Harris function based active contour external force for image segmentation
Pattern Recognition Letters
A multiple object geometric deformable model for image segmentation
Computer Vision and Image Understanding
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Active contour and active polygon models have been used widely for image segmentation. In some applications, the topology of the object(s) to be detected from an image is known a priori, despite a complex unknown geometry, and it is important that the active contour or polygon maintain the desired topology. In this work, we construct a novel geometric flow that can be added to image-based evolutions of active contours and polygons in order to preserve the topology of the initial contour or polygon. We emphasize that, unlike other methods for topology preservation, the proposed geometric flow continually adjusts the geometry of the original evolution in a gradual and graceful manner so as to prevent a topology change long before the curve or polygon becomes close to topology change. The flow also serves as a global regularity term for the evolving contour, and has smoothness properties similar to curvature flow. These properties of gradually adjusting the original flow and global regularization prevent geometrical inaccuracies common with simple discrete topology preservation schemes. The proposed topology preserving geometric flow is the gradient flow arising from an energy that is based on electrostatic principles. The evolution of a single point on the contour depends on all other points of the contour, which is different from traditional curve evolutions in the computer vision literature