A Practical Method for Circular Hole Measurement Based on Machine Vision

Yan Jin; Yong Liang Zhou; Biao Chen

doi:10.4028/www.scientific.net/AMR.317-319.837

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 317-319A Practical Method for Circular Hole Measurement...

A Practical Method for Circular Hole Measurement Based on Machine Vision

Abstract:

Non-contact Measurement of circular hole, which based on machine vision is already used for industrial applications. In this paper, a novel practical method to obtain parameters of a circular hole from an image is presented. The main idea of the proposed method is that we use gradient information to find a region that contains possible centers of the circle (the hole) and use evidence-collecting process to obtain the reasonable radius. This method fits the situation that the hole object in a image is totally brighter or darker than its background. Experimental results demonstrate that our method can detect the hole with the acceptable precision and stability for practical applications.

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Periodical:

Advanced Materials Research (Volumes 317-319)

Pages:

837-842

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.317-319.837

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Online since:

August 2011

Authors:

Yan Jin, Yong Liang Zhou, Biao Chen

Keywords:

Circle Measurement, Hough Transform (HT), Machine Vision, Voting Method

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References

[1] Elias N.Malamas, Euripides G.M. Petrakis, Michalis Zervakis, Laurent Petit, Jean-Didier Legat: Image and Vision Computing. Vol. 21 (2003), p.171

DOI: 10.1016/s0262-8856(02)00152-x

Google Scholar

[2] Richard O. Duda, Peter E. Hart E: Communications of the ACM. Vol.15 (1972), p.11

Google Scholar

[3] Peter E.Hart: IEEE Signal Processing Magazine. November 2009, p.18

Google Scholar

[4] J.Illingworth, J.Kittler: Computer Vision, Graphics , and Image Process. Vol.43 (1988), p.87

Google Scholar

[5] Kimme C.,Ballard D. and Sklansky: Communications of the ACM. Vol. 18 (1975), p.120

Google Scholar

[6] W.C.Y. Lam, S.Y. Yuen: IEEE Proc.-Vis. Image Signal Proces.Vol. 143(1996), p.292

Google Scholar

[7] H.K. Yuen, J.Princen, J.Illingworthand J.Kittler: Image Vis.Comput.Vol.8 (l990), p.71

Google Scholar

[8] J.Illingworth, J.Kittler: IEEE Trans. Pattern Anal. Mach.Intell. Vol. 9(1987), p.690

Google Scholar

[9] Shang Fei, Wang Fenggui, Tian Di and Zhao Zhihui: Comput.Acta Optica Sinica. Vol.28 (2008), p.739 (In Chinese)

Google Scholar

[10] Lei Xu, E.Oja and P. Kultanen: Pattern Recognition Letters.Vol.57 (1990), p.131

Google Scholar

[11] Teh-Chuan Chen, Kuo-Liang Chung: Computer Vision and Image Understanding Vol. 83(2001), p.172

Google Scholar

[12] Shih-Hsuan Chiu, Jiun-Jian Liaw: Pattern Recognition Letters. Vol.26 (2005), p.121

Google Scholar