为了准确识别出背景复杂、对比度低时的输电导线目标,在对图像进行全变分模型去噪的基础上,提出一种基于模拟退火微粒群算法的2维最大类间方差法进行图像分割,然后采用改进的Freeman链码表示法进行输电导线目标提取,并应用基于最小二乘法的直线拟合法复原输电导线的基本骨架中的缺失部分。实验结果表明:基于模拟退火微粒群算法的2维最大类间方差法具有较好的分割效果,且在最佳分割阈值的搜索中有着较好的收敛性和计算速度;基于改进的Freeman链码表示法的输电导线提取算法可以很好地滤除背景并将输电导线完整地提取出来。

In order to identify transmission lines accurately from low-contrast images with complex backgrounds,we applied the total variation denoising model.A two-dimensional explain based on the Simulated Annealing and Particle Swarm Optimization Algorithm is proposed and used for image segmenation.Besides,the traditional Freeman Chain Code Representation Method is improved to extract the transmission line target,and a line fitting method based on the Least Square Method is used to recover missing parts of the basic skeleton of the transmission lines.The experimental results indicate that the two-dimensional OTSU based on the Simulated Annealing and Particle Swarm Optimization Algorithm has better segmentation effect with better convergence and higher computing speed in the search of the best segmentation threshold.The transmission lines extraction algorithm based on the improved traditional Freeman Chain Code Representation Method can remove the complex image background well and extract transmission lines completely.