基于活动轮廓模型的手写体汉字快速分割算法

Lei Zhu, Jing Yang
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摘要

经典的C-V算法存在多次迭代操作和计算时间过长的缺点,无法分割大尺寸图像。在分析图像大小与得到正确结果的迭代次数和时间之间的关系的基础上,提出了一种基于局部C-V活动轮廓模型的快速图像分割算法,该算法基于阈值分割和连通分量标记。首先采用OTSU方法对图像进行粗分割,然后采用连通域快速非递归像素标记算法对图像进行标记和切割。在C-V模型中,分割被用作初始解决方案。分析和实验结果表明,与经典的C-V算法相比,改进的C-V算法可以快速得到正确的结果。对轮廓细节最深刻的大尺寸图像进行分割是快速有效的。
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Fast Handwritten Chinese Characters Segmentation Algorithm Based on Active Contour Model
The classical C-V algorithm has the shortage about multi-iterative operations and the computational time is too long to segment the large size image. On the base of analysis upon the relationship between the image size and the number of iterations and time to get the right result, the article proposes a fast image segmentation algorithm based on local C-V active contour model which are based on threshold segmentation and the connected component labeling. In the first step, a coarse segmentation is obtained by using the OTSU method, then label and cut the image with the fast non-recursion pixel marking algorithm of connected domains. The segmentation is used as an initial solution in the C-V model. The analysis and experimental results indicate that the improved C-V algorithm can get the right result quickly compared with classical C-V algorithm. It is fast and effective to segment the large size image which has most profound contour details.
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