Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object. When a collection of possible shapes is available, the specification of a shape prior using kernel density estimation is a natural technique. Unfortunately, energy functionals arising from kernel density estimation are of a form that makes them impossible to directly minimize using efficient optimization algorithms such as graph cuts. Our main contribution is to show how one may recast the energy functional into a form that is minimizable iteratively and efficiently using graph cuts.

中文翻译：

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具有非线性统计形状先验的图像分割的迭代图切割。


基于形状的正则化已被证明是一种在噪声图像中描绘对象的有用方法，其中人们对目标对象的形状有先验知识。当可能形状的集合可用时，先使用核密度估计来指定形状是一种自然的技术。不幸的是，核密度估计产生的能量泛函的形式使得它们不可能使用有效的优化算法（例如图割）直接最小化。我们的主要贡献是展示如何使用图割将能量泛函重新转换为一种可迭代且有效地最小化的形式。