An Efficient Active Contour Model Through Curvature Scale Space Filtering
Multimedia Tools and Applications
CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Frequency Domain Formulation of Active Parametric Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally regularized smoothing B-snake
EURASIP Journal on Applied Signal Processing
Robust boundary delineation using random-phase-shift active contours
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Boundary reconstruction in binary images using splines
Pattern Recognition
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Energy-minimizing active contour models or snakes can be used in many applications such as edge detection, motion tracking, image matching, computer vision, and three-dimensional (3-D) reconstruction. We present a novel snake that is superior both in accuracy and convergence speed over previous snake algorithms. High performance is achieved by using spline representation and dividing the energy-minimization process into multiple stages. The first stage is designed to optimize the convergence speed in order to allow the snake to quickly approach the minimum-energy state. The second stage is devoted to snake refinement and to local minimization of energy, thereby driving the snake to a quasiminimum-energy state. The third stage uses the Bellman (1957) optimality principle to fine-tune the snake to the global minimum-energy state. This three-stage scheme is optimized for both accuracy and speed