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低对比度叠层薄片的机器视觉计数测量方法

肖昌炎 邱华林 赵宏 曹三 蒋仕龙

湖南大学学报(自然科学版)2018,Vol.45Issue(4):122-128,7.
湖南大学学报(自然科学版)2018,Vol.45Issue(4):122-128,7.DOI:10.16339/j.cnki.hdxbzkb.2018.04.016

低对比度叠层薄片的机器视觉计数测量方法

A Count Measurement Method for Low Contrast Stacked Sheets in Machine Vision

肖昌炎 1邱华林 1赵宏 1曹三 1蒋仕龙2

作者信息

  • 1. 湖南大学 电气与信息工程学院,湖南 长沙 410082
  • 2. 深港产学研基地,广东 深圳 518000
  • 折叠

摘要

Abstract

To solve the problem of sheets counting in the printing industry and so on,the count meas-urement method based on machine vision is proposed.Image acquisition system is built by using the latest high-resolution CMOS camera and megapixel,low distortion of the fixed focus lens.Aiming at the prob-lem of detecting low contrast stacked sheets,a local histogram equalization algorithm and a linear enhance-ment based on bi-Gaussian second order are proposed.The algorithm is divided into the following parts. First,to improve the speed of the algorithm,only part of the image,which is obtained from image acquisi-tion platform,is selected as processing area,and wavelet de-noising is performed.Second,the prepro-cessed image is enhanced by the local histogram equalization.Then,the detecting of the stacked sheets is acquired by using second-order derivative of bi-Gaussian and mean filter processing in the horizontal and vertical directions,respectively.Finally,the counting result can be acquired by the peak detection,and displayed in time.Experimental results show that the error rate of the count measurement method is less than 0.01% for the various types of sheets (including the 0.05 mm pure white sheets).It can also save a lot of manpower and material sources.Therefore,it deserves being promoted and used.

关键词

低对比度/薄片计数/局部直方图均衡化/双高斯/峰值检测

Key words

low contrast/sheets counting/local histogram equalization/bi-Gaussian/peak detection

分类

信息技术与安全科学

引用本文复制引用

肖昌炎,邱华林,赵宏,曹三,蒋仕龙..低对比度叠层薄片的机器视觉计数测量方法[J].湖南大学学报(自然科学版),2018,45(4):122-128,7.

基金项目

国家自然科学基金资助项目(61571184,U1613209),National Natural Science Foundation of China(61571184,U1613209) (61571184,U1613209)

湖南大学学报(自然科学版)

OA北大核心CSCDCSTPCD

1674-2974

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