基于改进C-V方法的焊接图像识别

基于简化的Mumford—Shah水平集图像分割模型，Chart—Vese提出了不依赖于图像边缘的水平集图像分割算法（C～V方法）。文中对该算法进行了深入研究，指出了原方法存在的缺陷，即处理的图像必须具有比较明显的特征，分割目标过多且较为分散时则很难得到理想的结果，每次迭代过程都需要对所有的图像数据进行计算，比较费时。根据焊接图像本身的特点给出了三点改进，即强化特征模型的修正、多尺度快速算法和全局特性抑制。应用改进的算法，进行了模拟对比试验和真实熔池图像识别的试验。结果表明，该方法能识别出焊接图像连续轮廓，提取有用信息，具有良好的适应性，同时为复杂图像的特征物体目标提取提供了可行的思路。

Recognition of welding image based on improved C-V method

The level set image segmentation method (C-V method) was proposed by Chan and Vese based on Mumford-Shah model.Its limitation was analyzed and the improved method was provided according to the character of welding environment.It has three mainly limitation, that is, the image must have distinct characters; the target to be divided can not be too many and dispread;it coss excessive time.The improved algorithms include modification of strengthened character model, multi-scale rapid calculation and suppression of global characters.The comparison test and lab experiment in real welding environment were given.The results prove the improved algorithm can extract feature accurately and adapt to environment changes.The improved algorithms also provide a good way to deal with image character extracted for complex images.