Laser edge image inpainting based on pixel adjacency analysis

TAN Jian-ping1; 2; WANG Xian1

doi:10.3788/OPE.20132110.2728

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Laser edge image inpainting based on pixel adjacency analysis

更新时间：2020-08-12

- Laser edge image inpainting based on pixel adjacency analysis
- Optics and Precision Engineering Vol. 21, Issue 10, Pages: 2728-2735(2013)
- 作者机构：
  
  1. 中南大学机电工程学院，湖南长沙410083
  2. 中南大学高性能复杂制造国家重点实验室，湖南长沙 410083
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132110.2728
  CLC： TP391
- Received：27 February 2013，
  
  Revised：08 May 2013，
  
  Published Online：19 October 2012，
  
  Published：15 October 2013
- 稿件说明：
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王宪谭建平. 应用像素邻接特性分析的激光边缘图像修复[J]. 光学精密工程, 2013,21(10): 2728-2735

TAN Jian-ping WANG Xian. Laser edge image inpainting based on pixel adjacency analysis[J]. Editorial Office of Optics and Precision Engineering, 2013,21(10): 2728-2735
王宪谭建平. 应用像素邻接特性分析的激光边缘图像修复[J]. 光学精密工程, 2013,21(10): 2728-2735 DOI： 10.3788/OPE.20132110.2728.

TAN Jian-ping WANG Xian. Laser edge image inpainting based on pixel adjacency analysis[J]. Editorial Office of Optics and Precision Engineering, 2013,21(10): 2728-2735 DOI： 10.3788/OPE.20132110.2728.

摘要

针对现有边界提取方法用于复杂工业环境的不足，提出了一种应用像素邻接特性分析的光斑边缘图像修复方法。首先，通过对边缘图像的距离变换和连通分量标记得到一张标号图像，该图像把与最近边缘距离低于某一数值的背景像素标注为边缘候选点，其他背景像素标注为独立的连通区域。然后，依据真实边缘的邻接特性对候选边缘候选点重标号，实现断裂边缘的连接。最后，从邻接特性的角度对噪声进行分类并去除，从而完成激光边缘图像的修复。实验结果表明：该方法能有效修复8 pixel的边缘缝隙并去除较大的噪声；引入的中心定位均方根（RMS）误差为0.05 pixel ，峰值(PV)误差为0.086 pixel，稳定地保持在较低的水平；单次图像修复耗时小于130 ms

实时性较好；能用于工业在线中心定位检测。

Abstract

As existing edge extracting method has its drawbacks in complex industrial environments

a laser edge image inpainting method based on pixel adjacency analysis is presented. The first step of the method is to obtain a label image by distance conversion and connected component mark for an edge image. In the label image

the background pixels whose minimum edge distances are less than a certain value are marked as edge candidate points while the rest background pixels are marked as independent connected regions. The second step is to re-label the edge candidate points based on the adjacency of real edge to connect the broken edges. And the final step is to categorize the noises as per adjacency and remove them respectively to inpaint the laser edge image. The experimental results indicate that this method can effectively inpaint a 8-pixel edge gap and remove larger noises; the introduced root-mean-square（RMS）error of centering measurement is 0.05 pixel

while the peak value（PV） error is 0.086 pixel

which show a steadily low value. Moreover

it takes less than 130 ms for an image to be inpainted one time in real-time. This method is applicable to industrial online centering measurement.

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references

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