Abstract

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. 1 Introduction The active contour models (snakes) [1] turn boundary detection into an optimization problem. Given a snake v = fv i = (x i ; y i ) : i = 1; 2; : : :; ng one seeks to minimize its energy: e(v) = n X i=1 i E int (v i ) + (1 \Gamma i )E ext (v i ) (1) where the internal energy E int imposes continuity and smoothness constraints; and the external energy E...

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