Keyboard Quality Automated Inspection Based on Machine Vision

Yue Hua Gao; Bing Luo

doi:10.4028/www.scientific.net/AMR.765-767.1877

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767Keyboard Quality Automated Inspection Based on...

Keyboard Quality Automated Inspection Based on Machine Vision

Abstract:

Because traditional keyboard quality manual inspection is inefficient and low reliability for subjectivity, machine vision based automated inspection system was developed. Keyboards fine difference in position and rotation angles existing in captured images influenced image matching. Hough transform was applied to detect the edges of keyboards for their image calibration. Many small image templates for each key and a global template for entire keyboard were set up for automated inspecting character printing quality with moderate difference sufferance. Two CCD cameras were used for image capturing to deal with keyboard big area and software was developed on HexSight Image Kit. Experimental results showed that keyboard quality can be effectively inspected with better performance.

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Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

1877-1880

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.765-767.1877

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Online since:

September 2013

Authors:

Yue Hua Gao, Bing Luo

Keywords:

Image Matching, Image Processing, Machine Vision, Quality Inspection

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References

[1] Hebel , Chris , Disabatino , Ron , Gresh , Kate. Machine Vision Technology. [J]. Nuclear Plant Journal, 2012, Vol. 30 (1).

Google Scholar

[2] Wilson , Andrew. 3-D Machine Vision Guides Robots into Action. [J]. Vision Systems Design, 2013, Vol. 18 (1).

Google Scholar

[3] FOKKINGA MAARTEN. The Hough transform. [J]. Journal of Functional Programming, 2011, Vol. 21 (2), pp.129-133.

Google Scholar

[4] Yan , Zhiguo , Xu , De , Tan , Min. A fast and robust method for line detection based on image pyramid and Hough transform. [J]. Transactions of the Institute of Measurement and Control, 2011, Vol. 33 (8).

DOI: 10.1177/0142331210361560

Google Scholar

[5] Messom C. H, Sen Gupta G, Demidenko S.N. Hough. Transform Run Length Encoding for Real-Time Image Processing. IEEE Transactions on Instrumentation and Measurement . (2007).

DOI: 10.1109/tim.2006.887687

Google Scholar

[6] Jen-Hui Chuang. Shape matching and recognition using a physically based object model. [J]. Computers & Graphics, 2001, 25(2).

DOI: 10.1016/s0097-8493(00)00125-4

Google Scholar

[7] Liang Hu , Manning Wang, Zhijian Song. Manifold-based feature point matching for multimodal image registration. [J]. Int J Med Robotics Comput Assist Surg, 2012, Vol. 9 (1).

DOI: 10.1002/rcs.1465

Google Scholar

[8] Sinisa Todorovic , Narendra Ahuja Region-Based Hierarchical Image Matching. [J]. International Journal of Computer Vision, 2008, Vol. 78 (1).

Google Scholar