首页> 外文会议>2009 IEEE 12th International Conference on Computer Vision (ICCV 2009) >Curvature regularity for region-based image segmentation and inpainting: A linear programming relaxation
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Curvature regularity for region-based image segmentation and inpainting: A linear programming relaxation

机译:基于区域的图像分割和修复的曲率规律性:线性规划松弛

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We consider a class of region-based energies for image segmentation and inpainting which combine region integrals with curvature regularity of the region boundary. To minimize such energies, we formulate an integer linear program which jointly estimates regions and their boundaries. Curvature regularity is imposed by respective costs on pairs of adjacent boundary segments. By solving the associated linear programming relaxation and thresholding the solution one obtains an approximate solution to the original integer problem. To our knowledge this is the first approach to impose curvature regularity in region-based formulations in a manner that is independent of initialization and allows to compute a bound on the optimal energy. In a variety of experiments on segmentation and inpainting, we demonstrate the advantages of higher-order regularity. Moreover, we demonstrate that for most experiments the optimality gap is smaller than 2% of the global optimum. For many instances we are even able to compute the global optimum.
机译:我们考虑一类基于区域的能量,用于图像分割和修复,这些能量将区域积分与区域边界的曲率规律性相结合。为了使这种能量最小化,我们制定了一个整数线性程序,该程序可以联合估算区域及其边界。曲率规律性是由成对的相邻边界线段上的相应成本决定的。通过求解相关的线性规划松弛和阈值求解,可以得出原始整数问题的近似解。据我们所知,这是第一种以独立于初始化并允许计算最佳能量边界的方式在基于区域的公式中施加曲率规则性的方法。在分割和修复的各种实验中,我们展示了高阶规则性的优点。此外,我们证明,对于大多数实验而言,最佳差距小于全局最佳的2%。在许多情况下,我们甚至可以计算全局最优值。

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