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Graph Cut Optimization for the Piecewise Constant Level Set Method Applied to Multiphase Image Segmentation

机译:分段恒定水平集方法在多相图像分割中的图割优化

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The piecewise constant level set method (PCLSM) has recently emerged as a variant of the level set method for variational inter-phase problems. Traditionally, the Euler-Lagrange equations are solved by some iterative numerical method for PDEs. Normally the speed is slow. In this work, we focus on the piecewise constant level set method (PCLSM) applied to the multiphase Mumford-Shah model for image segmentation. Instead of solving the Euler-Lagrange equations of the resulting minimization problem, we propose an efficient combinatorial optimization technique, based on graph cuts. Because of a simplification of the length term in the energy induced by the PCLSM, the minimization problem is not NP hard. Numerical experiments on image segmentation demonstrate that the new approach is very superior in terms of efficiency, while maintaining the same quality.
机译:分段恒定水平集方法(PCLSM)最近作为变集相间问题的水平集方法的一种变体而出现。传统上,通过某些PDE的迭代数值方法来求解Euler-Lagrange方程。通常速度很慢。在这项工作中,我们集中于应用于多相Mumford-Shah模型的分段恒定水平集方法(PCLSM)进行图像分割。代替求解所得最小化问题的Euler-Lagrange方程,我们提出了一种基于图割的有效组合优化技术。由于简化了PCLSM感应的能量中的长度项,因此最小化问题并不是NP难题。图像分割的数值实验表明,该新方法在保持相同质量的同时,在效率方面非常优越。

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