We present the first method to handle curvature regularity in region-based image segmentation and inpainting that is independent of initialization. To this end we start from a new formulation of length-based optimization schemes, based on surface continuation constraints, and discuss the connections to existing schemes. The formulation is based on a \emph{cell complex} and considers basic regions and boundary elements. The corresponding optimization problem is cast as an integer linear program. We then show how the method can be extended to include curvature regularity, again cast as an integer linear program. Here, we are considering pairs of boundary elements to reflect curvature. Moreover, a constraint set is derived to ensure that the boundary variables indeed reflect the boundary of the regions described by the region variables. We show that by solving the linear programming relaxation one gets quite close to the global optimum, and that curvature regularity is indeed much better suited in the presence of long and thin objects compared to standard length regularity.

本文提出了一种使用基于单元复合的整数线性规划方案来处理图像分割中的曲率规则，从而实现对初始值的独立处理，并将问题建模为图像分割和修复的整数线性规划问题，通过该模型得到的结果在处理长而细的对象的曲率规则时比标准的长度规则更适用。

基于曲率惩罚的基于区域的图像分割和修复的线性框架