快速并行细化算法在焊缝识别中的实现

针对焊接部分和其他部分的特征区分不明显，在焊缝质量检测的图像采集中可能会存在大量噪点从而导致焊缝识别出现偏差的问题，提出一种基于图像快速并行细化的焊缝识别方法。文章给出图像获得、图像灰度转化、图像二值化、图像缩小、图像快速并行细化的处理步骤及采用C#语言对焊缝识别的编程实现，并对实际焊缝图像进行实验验证。该算法对图像的焊缝骨架提取较为准确，同时具有运行效率高和易于移植的特点。  

Application of Fast Parallel Thinning Algorithm in Seam Tracking

Since distinguishing features are not obvious between the welding seam quality testing section and other parts , there is a lot of noise in the image acquisition which leads deviations for weld identification. The authors put forward a new method to identify the appropriate solutions. This paper presents the approaches for image processing steps such as images obtaining, image gray transformation, image binarization, image reduction, and image fast parallel thinning. The programming language C# is used to implement weld seam for effective image recognition. The experimental results show that the method can achieve fast recognition, attain high accu- racy and is easy to transplant.