彩色树木图像分割算法的研究

树木图像分割是将树木与其周围景物分离的技术，是虚拟现实和计算机仿真等学科在林业应用的核心技术，也是机器视觉领域的重要研究方向，拓宽了计算机技术在林业中的应用。本项研究基于树木图像形状复杂的特点，设计并实现了一种结合C-V模型水平集及形态学处理的彩色树木图像分割算法。运用改进的最小化能量函数作为水平集的演化曲线，可以更加自然地改变曲线拓扑结构，对含有分裂、合并、形成尖角等复杂形状的目标对象分割更为有效。如果再结合形态学后处理算法，将初次分割图像中非目标区的细密纹理和噪声剔除，可以快速准确地得到全局最优的图像分割效果。同时进行了与基于梯度变换的改进分水岭树木图像分割和基于灰度-梯度最大熵的树木图像分割算法的对比试验，试验表明，结合C-V模型水平集和形态学算法对树木图像分割效果更为有效。

Research on Color Trees Image Segmentation

The trees image segmentation is a technology separating trees from its surrounding landscape, which is the core technology in virtual reality and computer simulation of forestry application and also is one of the focus are-as in machine vision to provide basic data and technical support for the application of computer technology in forest-ry.Base on characteristics of complex shapes of the trees image, this paper designed and implemented a color im-age segmentation based on a set level of C-V model and later morphological processing operation.The improved minimization of energy function was made as the level set evolution curve, because it could naturally change the e-volution curve, and also more effectively segment complex shapes with parts of fission, mergence and sharp corner. Then combined with the later morphological processing operation, which could clear the non-target parts such as texture and noise from the initial image segmentation, finally the global optimal image segmentation could be ob-tained fast and accurately.At the same time, we compared the color trees image segmentation, combined with a set with level of C-V model and the later morphological processing operation, with the improved watershed trees image segmentation based on gradient transform and the maximum entropy trees image segmentation based on gray gradi-ent.The results showed that the method combined a set level of C-V model and the later morphological processing operation was more effective.