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Multi-region level set image segmentation based on image cartoon-texture decomposition model

机译:基于图像卡通纹理分解模型的多区域水平集图像分割

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This paper presents a multi-region level set image segmentation method based on image cartoon - texture decomposition model. The image feature is extracted by using the image decomposition method. We represent the regions by the level set functions with constraint. The coupled Partial Differential Equations (PDE) related to the minimization of the functional are considered through a dynamical scheme. A modified region competition factor is introduced to guarantee no vacuum and non-overlapping between the neighbor regions, it also speed up the cure evolution functions, the final segmentation can be achieved after several iterations. Several experiments are conducted on both synthetic images and natural images, the results illustrate that the proposed multi-region segmentation method is fast and less sensitive to the initializations.
机译:本文提出了一种基于图像卡通-纹理分解模型的多区域水平集图像分割方法。通过使用图像分解方法提取图像特征。我们通过具有约束的水平集函数来表示区域。通过动力学方案考虑了与泛函的最小化有关的耦合偏微分方程(PDE)。引入了修改后的区域竞争因子,以确保相邻区域之间不存在真空和不重叠,还加快了固化演化功能,经过多次迭代即可实现最终分割。在合成图像和自然图像上进行了几次实验,结果表明所提出的多区域分割方法快速且对初始化不敏感。

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