Using the Split Bregman Algorithm to Solve the Self-Repelling Snake Model
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Preserving the contour topology during image segmentation is useful in manypractical scenarios. By keeping the contours isomorphic, it is possible to pre-vent over-segmentation and under-segmentation, as well as to adhere to giventopologies. The self-repelling snake model (SR) is a variational model thatpreserves contour topology by combining a non-local repulsion term with thegeodesic active contour model (GAC). The SR is traditionally solved using theadditive operator splitting (AOS) scheme. Although this solution is stable, thememory requirement grows quickly as the image size increases. In our paper,we propose an alternative solution to the SR using the Split Bregman method.Our algorithm breaks the problem down into simpler subproblems to use lower-order evolution equations and approximation schemes. The memory usage issignificantly reduced as a result. Experiments show comparable performance to the original algorithm with shorter iteration times.
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