Machine vision based 2D measurement method for industrial glass

Chen Zhou; Hanyu Hong; Xiuhua Zhang; Shuhan Zhao; Pan Chen

doi:10.1117/12.2539408

14 February 2020 Machine vision based 2D measurement method for industrial glass

Chen Zhou, Hanyu Hong, Xiuhua Zhang, Shuhan Zhao, Pan Chen

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114301F (2020) https://doi.org/10.1117/12.2539408
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

In order to achieve high efficiency, automatic and accurate measurement, the paper takes the two-dimensional measurement of industrial glass under the experimental conditions. The main contents of this paper includes: Analyzing the structure and hardware performance parameters of the system, building a measuring platform including computer, Charge-coupled Device image sensor, lens, etc., using high-precision camera to take the image of glass, preprocessing of glass image data and acquiring edge information of glass. The system use second filtering method to filter the image and Canny operator to acquire the edge of the industry glass, transforming computer coordinate system into world coordinate system through coordinate transformation method, and finally calculate the two-dimensional size information of industrial glass. The system measures the two-dimensional length and width of polygonal glass, the experimental results show that the measurement method in this paper meet the accuracy requirements of general industrial measurement, and the detection system is feasible.

Citation Download Citation

Chen Zhou, Hanyu Hong, Xiuhua Zhang, Shuhan Zhao, and Pan Chen "Machine vision based 2D measurement method for industrial glass", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301F (14 February 2020); https://doi.org/10.1117/12.2539408

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available