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Signal-walking-driven active contour model

机译：信号行走驱动的主动轮廓模型

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Active contour models are effective image segmentation methods. However, they are very time-consuming, and their convergence depends upon the choice of initial contour. To overcome the two drawbacks, in the study, the authors suggest a signal-walking-driven active contour model. By walking a signal, they construct a forest of object evolution. Each tree grows from a root object, and child node contains its shrunk or/and split version. The merit value of an object is a composite metric from the colour, edge, or/and shape properties. The merit function plays an important role in tree construction and the goodness of object evolution. The objects are selected and added to the tree in the levels the merit function reaches the local maxima. After the forest of object evolution is constructed, by traversing each tree branch in post-order, the objects corresponding to maximum merit values are extracted as the final segmentation. Experimental results on a set of oil-sand images indicate the proposed signal-walking-driven active contour model outperforms Chan and Vese's model and adaptive thresholding.
机译：活动轮廓模型是有效的图像分割方法。但是，它们非常耗时，并且它们的收敛取决于初始轮廓的选择。为了克服这两个缺点，研究人员提出了一种信号行走驱动的主动轮廓模型。通过发出信号，他们构建了对象进化的森林。每棵树均从根对象生长而来，子节点包含其缩小或/和拆分版本。对象的价值是根据颜色，边缘或/和形状属性的综合指标。优点函数在树的构建和对象演化的良好性中起着重要作用。选择对象并将其添加到功能函数达到局部最大值的级别。在构造了对象演化森林之后，通过以后顺序遍历每个树枝，提取与最大优值对应的对象作为最终分割。在一组油砂图像上的实验结果表明，所提出的信号行走驱动的主动轮廓模型优于Chan和Vese的模型以及自适应阈值。

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