局部C V主动轮廓模型快速图像分割算法

经典的C V模型分割算法在处理较大尺寸图像时存在需多次迭代、运算时间长的缺点。在分析图像尺寸和初始逼近图像与获得稳定解的迭代次数与运算时间的关系的基础上，提出了一种改进的基于阈值分割及快速连通域标记算法的局部C V图像分割算法，对大尺寸图像进行处理。采用OTSU算法对图像进行初步的阈值分割，再利用快速非递归连通域标记算法进行连通域的标记及图像的局部分片。对分片后的小块图像以其阈值分割的结果作为初始逼近图像采用C V算法进行分割处理。算法分析及仿真结果证实，与经典C V算法相比较，改进的算法能够以很少的迭代次数和很短的运算时间达到稳定解，能够对含有丰富轮廓细节的大尺寸图像进行快速有效的处理。

A fast image segmentation algorithm with local C V active contour model

For the typical C V Algorithm, there exists the weakness of requiring multi iterative operations and long time computation to deal with large size image. Based on the analysis upon the relationship between the image size and the initialized approaching image with the number of iterations and computing time to obtain the stead results, an improved local C V image divisional algorithm based on the segmentation of threshold value and the connected domain labeled algorithm to deal with large size image is proposed. The OTSU method is used to divide the threshold value of image to reach the goal of label and local segmentation of image through the fast non recursion algorithm of connected domains. The segmented pieces and the result of its segmentation are used as the initialized approaching image of the C V algorithm model. Compared with the classical C V algorithm, the analysis and simulated result indicates that the improved C V algorithm reaches the steady solution quickly with fewer times of iterations. The proposed method can handle large size image with profound contour details quickly and effectively.