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A Novel Image Segmentation Algorithm Based on Improved Active Contour Model

机译:一种基于改进的活动轮廓模型的新型图像分割算法

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During executing the image segmentation algorithm based on classical geometric active contour model, to obtain accurate segmentation results always involves a redundantly iterative process. And what's more, sometimes this tedious iteration does not make the algorithm converge on the desired edge and even brings out some overshoot. To improve the segmentation efficiency and accuracy, a novel image segmentation algorithm was presented. First, the gradient image is calculated out based on the vector-valued image and then an adaptive edge indicator is proposed. Second, the revised active contour evolution model using variational level set method is put forward. The experiments demonstrate that the model has significantly increased the convergence rate and accuracy. And the proposed segmentation algorithm has also greatly improved the flexibility of the control of active contour evolution by means of its adaptive parameters adjustment.
机译:在基于经典几何活动轮廓模型执行图像分割算法期间,获得准确的分段结果始终涉及冗余迭代过程。更重要的是,有时这种繁琐的迭代不会使算法会聚在所需的边缘上,甚至会带出一些过冲。为了提高分割效率和准确性,提出了一种新颖的图像分割算法。首先,基于矢量值图像计算梯度图像,然后提出自适应边缘指示符。其次,提出了使用变分级别设置方法的修改的主动轮廓演进模型。实验表明,该模型显着提高了收敛速度和准确性。并且所提出的分割算法还通过其自适应参数调整,大大提高了主动轮廓展开控制的灵活性。

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