Surface Roughness Extraction by Gibbs Random Fields of Laser Speckle Pattern Texture

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Based on computer texture analysis methods, the relationships between laser speckle texture features of grinding surfaces and surface roughness are investigated. The laser speckle texture pictures of different surface roughness are acquired by a simple equipment which consists of a digital camera and a diode laser. The texture method based on Gibbs Random Fields model is used to analyze laser speckle patterns. Gibbs texture features with the second-order neighborhood are extracted. The experiment results display that the surface roughness information included in the laser speckle texture pictures is monotonous withβ2~β5 Gibbs texture features. For comparing, normalized texture features has been done. This method can extract object’s surface roughness information which is the same material and machined by the same method through calibrating beforehand.

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515-520

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December 2012

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] D. J. Whitehouse, Handbook of surface and Nanometrology, Institute of Physics Publishing (IOP), (2003).

Google Scholar

[2] J. M. Bennett, Recent development in surface roughness characterization, Meas. Sci. Technol. Vol. 3, 1992, pp.1119-27.

Google Scholar

[3] D. J. White, Stylus contact method for surface metrology in the ascendancy, Meas Control, Vol. 31, No. 2, 1998, pp.48-50.

DOI: 10.1177/002029409803100204

Google Scholar

[4] J. M. Bennett and L. Mattsson, Introduction surface roughness and scattering, Optical Society of America, (1993).

Google Scholar

[5] S. I. Chang, J. S. Ravathur, Computer vision based non-contact surface roughness assessment using wavelet transform and response surface methodology, Quality Engineering, Vol. 17, 2005, pp.735-451.

DOI: 10.1081/qen-200059881

Google Scholar

[6] R. Kumar, P. Kulashekar, B. Dhanasekar, B. Ramamoorthy, Application of digital image magnification for surface roughness evaluation using machine vision, Int. J. of Machine Tools and Manufacture, Vol. 45, 2005, pp.228-234.

DOI: 10.1016/j.ijmachtools.2004.07.001

Google Scholar

[7] L. C. Leonard, V. Toal, Roughness measurement of metallic surfaces based on the laser speckle contrast method, Optics and Lasers in Engineering. Vol. 30, pp.433-440, (1998).

DOI: 10.1016/s0143-8166(98)00036-0

Google Scholar

[8] S. L. Toh, C. Quan, K. C. Woo, C. J. Tay, H.M. Shang, Whole field surface roughness measurement by laser speckle correlation technique, Optics & Laser Technology, Vol. 33, pp.427-434, (2001).

DOI: 10.1016/s0030-3992(01)00054-8

Google Scholar

[9] R. S. Lu, G. Y. Tian, On-line measurement of surface roughness by laser light scattering, Measurement science and technology, 17(2006) 1496-1520.

DOI: 10.1088/0957-0233/17/6/030

Google Scholar

[10] R. S. Lu, G. Y. Tian, Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture, Applied Optics, Vol. 45, No. 35, 2006, pp.1-9.

DOI: 10.1364/ao.45.008839

Google Scholar

[11] M. Tuceryan, A. K. Jain, Texture analysis, The Handbook of Pattern Recognition and Computer Vision, 2nd Edition, By C. H. Chen, L. F. Pau, P. S. P. Wang, World Scientific Publishing Co. 1998, pp.207-248.

DOI: 10.1142/9789812384737_0007

Google Scholar

[12] Haluk Derin, Howard Elliott, Modeling and segmentation of noisy and textured images using Gibbs Random Fields, IEEE transactions on pattern analysis and machine intelligence, vol. PAMI-9, No. 1, 1987, pp.39-55.

DOI: 10.1109/tpami.1987.4767871

Google Scholar

[13] Z. B. Zheng, Y. Q. Zhou, Markov random parameter estimate and image texture classification, Mapping transaction, vol. 24, No. 1, pp.45-51, Feb. (1995).

Google Scholar