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A multistage, optimal active contour model
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DOI：10.1109/83.541430.png)
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Abstract
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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 optimality principle to fine-tune the snake to the global minimum-energy state. This three-stage scheme is optimized for both accuracy and speed.
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