An active contour model for image segmentation based on elastic interaction
Journal of Computational Physics
Anisotropic virtual electric field for active contours
Pattern Recognition Letters
Robust B-spline Snakes For Ultrasound Image Segmentation
Journal of Signal Processing Systems
Active contour model via multi-population particle swarm optimization
Expert Systems with Applications: An International Journal
Gradient vector flow active contours with prior directional information
Pattern Recognition Letters
Multiscale cascade segmentation of deformable image and parameters evaluation
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
An effective approach to chin contour extraction
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Journal of Visual Communication and Image Representation
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Active contours or snakes are an effective edge-based method in segmenting an object of interest. However, the segmented boundary of a moving object in one video frame may lie far from the same moving object in the next frame due to its rapid motion, causing the snake to converge on the wrong edges. To guide the snake toward the appropriate edges, we have added gradient directional information to the external image force to create a “directional snake.” Thus, in minimizing the snake energy, the new method considers both the gradient strength and gradient direction of the image. Experimental results demonstrate that the directional snake can provide a better segmentation than the conventional method in certain situations, e.g., when there are multiple edge candidates in the neighborhood with different directions. The directional snake is significant because it provides a framework to incorporate directional information in digital video segmentation