[1]舒 军,李 灵,邓明舟. 基于NCC的PCB图像配准算法研究[J].湖北工业大学学报,2022,(2):48-54.
　SHU Jun,LI Ling,DENG Mingzhou. Research on PCB Image Registration Algorithm Based on NCC[J].,2022,(2):48-54.
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 基于NCC的PCB图像配准算法研究()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2022年第2期
页码:
48-54
栏目:
湖北工业大学学报
出版日期:
2022-04-29

文章信息/Info

Title:
 Research on PCB Image Registration Algorithm Based on NCC
文章编号:
1003-4684(2022)02-0048-07
作者:
 舒 军1;  李 灵2;  邓明舟2
 1 湖北工业大学电气与电子工程学院， 湖北 武汉 430068；
 2 湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室，湖北 武汉 430068
Author(s):
 SHU Jun1;  LI Ling2;  DENG Mingzhou2
 1 School of Electrical and Electronic Engineering, Hubei Univ. of Tech., Wuhan 430068, China;
 2 Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of[JZ]Energy Storage System, Wuhan 430068, China
关键词:
 ; PCB数码喷印;  归一化互相关匹配算法;  统计滤波;  动态规划;  广度优先搜索方法
Keywords:
 ; PCB digital printing;  normalized cross correlation matching algorithm;  statistical filtering;  dynamic programming;  breadth first search method
分类号:
TP391.4
文献标志码:
A
摘要:
 为解决PCB数码喷印中图像数据量大导致传统PCB图像配准时间复杂度高的问题，研究归一化互相关匹配算法（NCC），从候选点集和特征块选取两个角度对算法效率优化。利用统计滤波的方法滤除候选点中的误差点，减少计算量；分析PCB图像中配准点特点，利用动态规划的思路，并基于广度优先搜索方法，对特征块自适应选取，从而减少配准点集的遍历。实验中，利用分割后的基板图像与Gerber图像配准建立空间位置关系，再使用改进的配准算法来配准。实验表明，配准精度在99%左右，效率提高至1.6倍。
Abstract:
 In PCB digital printing, the large amount of image data leads to the high complexity of traditional PCB image registration, which is the main factor that affects the efficiency of the system. By studying the image registration algorithm, based on the normalized cross correlation matching algorithm (NCC), the efficiency of the algorithm is optimized from two perspectives of candidate point set and feature block selection. Statistical filtering is used to filter out the error points in the candidate points and reduce the amount of calculation; the characteristics of the registration points in the PCB image are analyzed. By using the idea of dynamic programming, and based on the breadth first search method, feature blocks are adaptively selected, thereby reducing the registration points Set traversal. Experiments show that this method can effectively improve the traditional normalized cross correlation registration method, that the registration accuracy is about 99%, and that the efficiency is increased by 1.6 times.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2021-08-20
［第一作者］ 舒 军（1973-），男，湖北荆门人，工学博士，湖北工业大学副教授，研究方向为机器视觉，深度学习
［通信作者］ 李 灵（1997-），女，湖北恩施人，湖北工业大学硕士研究生，研究方向为机器视觉，深度学习
更新日期/Last Update: 2022-04-28