Convergence analysis of active contours
Image and Vision Computing
Anisotropic virtual electric field for active contours
Pattern Recognition Letters
Automatic segmentation of femur bones in anterior-posterior pelvis x-ray images
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Gradient vector flow active contours with prior directional information
Pattern Recognition Letters
On the critical point of gradient vector flow snake
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Coarse-to-fine boundary location with a SOM-like method
IEEE Transactions on Neural Networks
Efficient numerical schemes for gradient vector flow
Pattern Recognition
Convolutional virtual electric field external force for active contours
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Harris function based active contour external force for image segmentation
Pattern Recognition Letters
Adaptive diffusion flow active contours for image segmentation
Computer Vision and Image Understanding
Computers & Mathematics with Applications
Journal of Visual Communication and Image Representation
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Snakes, or active contour models, have been widely used in image segmentation. However, most present snake models do not discern between positive and negative step edges. In this paper, a new type of dynamic external force for snakes named dynamic directional gradient vector flow (DDGVF) is proposed that uses this information for better performance. It makes use of the gradients in both x and y directions and deals with the external force field for the two directions separately. In snake deformation, the DDGVF field is utilized dynamically according to the orientation of snake in each iteration. Experimental results demonstrate that the DDGVF snake provides a much better segmentation than GVF snake in situations when edges of different directions are present which pose confusion for segmentation.