摘要
针对生产线上金属板表面光照不均匀和白、灰细颗粒相间的特点,将一维、二维Wellner自适应阈值算法应用到这种场景,并结合高斯加权距离,将算法推广,提出了一种针对金属板表面图像分割的高斯加权自适应分割算法。该方法首先通过计算区域内像素间的高斯加权距离,形成一张加权距离图,然后利用Wellner的"中心-周围比较思想"直接求算二值图像。最后对实际产线上采集的图片进行了实验,将二维otsu算法、均匀性度量算法、一维、二维Wellner自适应算法和最后改进的算法在分割效果进行比较。实验结果表明,相对于其他算法,最后应用的算法在分割效果上具有明显的优势。
For the problem of the characteristics of the grey and white granular background image condition on the surface of the plate metals,one-dimensional and two-dimensional Wellner adaptive threshold algorithm are the first time to be applied to the scene.Based on those two kinds of algorithm,this paper proposes a gaussian weighted adaptive threshold algorithm to solve the problem.Firstly,this algorithm calculates the gaussian weighted distance between pixels in a window to form a weighted distance diagram and then use the ideology of Wellner ＂ center-around comparison＂ to calculate binary image directly.Finally,experiments are carried out on images acquired on actual production line.The two-dimensional otsu algorithm,uniformity measurement algorithm,one-dimensional,twodimensional Wellner adaptive algorithm and the last improved algorithm are compared.Experimental results show that,compared with other algorithms,the algorithm in the end of this paper has obvious advantages in the image segmentation effect.
出处
《电子测量技术》
2017年第7期85-89,共5页
Electronic Measurement Technology
基金
上海市科委重大基础研究项目(14JC1402200)
上海市科委项目(15411953502)资助
关键词
阈值分割
不均匀表面
高斯加权距离
threshold segmentation
uneven surface
the Gaussian weighted distance
