基于圆形约束CV-LIF模型的原木端面图像分割

针对自然条件下原木端面图像的分割问题，结合原木端面图像的特点，改进传统CV（Chan and Vese）模型，对演化曲线内部使用梯度进行拟合，同时融入局部图像拟合LIF（Local Image Fitting）模型，加入圆形先验知识，提出了基于圆形约束的改进活动轮廓模型CV-LIF，将全局能量和局部能量结合到一起，共同约束轮廓线的演化。在对图像进行预分割的基础上，利用多水平集表示待分割区域，运用基于圆形约束的改进活动轮廓模型对每个水平集区域进行再分割，解决了复杂背景下多个原木端面分割不准确的问题。通过实验，分别对单个及多个原木端面图像进行分割，结果表明该方法可以较好地分割出图像中的原木端面，而且具有较好的抗噪性能，实现速度较快。

Log end image segmentation based on circle dependent CV-LIF model

The paper, in allusion to the segmentation problems of log end image obtained in natural conditions, and according to the characteristics of the log end image, improves the traditional CV（Chan and Vese） model, and fits the interior of evolving contour with gradient information. At the same time, with reference to the Local Image Fitting（LIF） model, and joining with circle prior knowledge, it proposes an improved active contour model based on circle dependent, which is called CV-LIF. The model combines global and local energy together to restrain the evolution of contours. On the basis of pre-segmentation image, it represents the regions which are to be segmented with multi-level set, and does re-segmentation to each level set region with the model proposed. This model solves the problem of inaccuracy of multiple logs ends image segmentation under the complex background. Experiments for single log end and multiple logs ends segmentation indicate that the model can segment the logs ends correctly, and has better anti-noise performance. It can effectively overcome the interference of background, and runs faster.