纱线毛羽骨架及长度的跟踪测量

针对现有图像法毛羽测量存在的缺陷，提出了一种用于毛羽分析和长度测量的纱线毛羽骨架跟踪算法。首先以10像素为步长，作纱线条干边缘曲线对毛羽骨架的分割线，得到毛羽起点；接着在毛羽延伸方向上对毛羽起点的上5?邻域点或下5?邻域点进行判断，得到新的毛羽路径点，进行邻域点的重复判断，直到没有毛羽路径点存在，依次记录所有毛羽点生成毛羽路径，并提出了多毛羽路径点和交叉毛羽的解决方案；最后根据2点间的距离计算出毛羽路径中相邻毛羽路径点的像素，从而得到毛羽的测量长度。对长毛羽的跟踪测量和固定分割长度测量的结果显示，毛羽骨架及长度的跟踪测量算法可将测量长度提高24.3%∽666%，测量结果较为精确。

Tracking measurement of yarn hairiness skeleton and length

Aiming at the defects on yarn hairiness measurement by the image process in the prior image methods, a new algorithm was proposed for analyzing hairiness and measuring hairiness length by tracking yarn hairiness skeleton. First, using 10 pixels as the step value, the dividing line of hairiness skeleton by the curves of yarn edge were plotted, and the hairiness starting points were obtained. And then, the upper 5-neighborhood points or lower 5-neighborhood points were judged in the extension direction of yarn hairiness, and the new hairiness path points were obtained. The all of path points were judged until no hairiness path points exist, all of which were recorded in sequence to generate a hairiness path, and the solutions of multi-hairiness path points and crossed hairiness were proposed. Finally, the pixel of two hairiness path points was calculated according to the distance between two points, and the hairiness measurement length was obtained. The results of tracking measurement and fixed segment measurement of long hairiness show that the measurement length can be increased by 24 . 3% -666% by the new method of yarn hairiness measurement using the hairiness tracking, and the measurement results are more accurate.