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On Regularization, Formulation and Initialization of Active Contour Models (Snakes)
K. F. Lai and R. T. Chin,
Proc. 1st Asian Conf. on Computer Vision, 1993, 542-545.
In snake formulation, large regularization enhances the robustness against noise and incomplete data, while small values increase the accuracy in capturing boundary variations. We present a local minimax criterion which automatically determines the optimal regularization at every locations along the boundary with no added computation cost. We also modify existing energy formulations to repair deficiencies in internal energy and improve performance in external energy. This yields snakes that contain Hough transform as a special case. We can therefore initialize the snake efficiently and reliably using Hough transform.