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A New Region-Edge Based Level Set Model with Applications to Image Segmentation

机译：基于区域边缘的新水平集模型及其在图像分割中的应用

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摘要

Level set model has advantages in handling complex shapes and topological changes, and is widely used in image processing tasks. The image segmentation oriented level set models can be grouped into region-based models and edge-based models, both of which have merits and drawbacks. Region-based level set model relies on fitting to color intensity of separated regions, but is not sensitive to edge information. Edge-based level set model evolves by fitting to local gradient information, but can get easily affected by noise. We propose a region-edge based level set model, which considers saliency information into energy function and fuses color intensity with local gradient information. The evolution of the proposed model is implemented by a hierarchical two-stage protocol, and the experimental results show flexible initialization, robust evolution and precise segmentation.
机译：水平集模型在处理复杂形状和拓扑变化方面具有优势，广泛用于图像处理任务。面向图像分割的水平集模型可以分为基于区域的模型和基于边缘的模型，两者都有优点和缺点。基于区域的水平集模型依赖于对分离区域的颜色强度的拟合，但对边缘信息不敏感。基于边缘的水平集模型通过拟合局部梯度信息而演变，但是很容易受到噪声的影响。我们提出了一种基于区域边缘的水平集模型，该模型将显着性信息考虑为能量函数，并将颜色强度与局部梯度信息融合在一起。该模型的发展是通过一个分层的两阶段协议来实现的，实验结果表明，该方法具有灵活的初始化，强大的演化能力和精确的分割效果。

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