Abstract

The existing machine-vision surface roughness measurement technique extracts relevant evaluation indices from grayscale images without using the strong sensitivity of color information. In addition, most of these measurements use a micro-vision imaging method to measure a small area and cannot make an overall assessment of the workpiece’s surface. To address these issues, a method of measuring surface roughness that uses an ordinary light source and a macro-vision perspective to generate a red and green color index for each pixel is proposed in the present study. A comparison test is conducted on a set of test samples before and after surface contamination using the color index and gray-level algebraic averaging, the square of the main component of the Fourier transform in the frequency domain, and the entropy. A strong correlation between the color index and the surface roughness is established; this correlation is not only higher than that of other indices but also present despite contamination and very robust. Verification using a regression model based on a support vector machine proves that the proposed method not only has a simple apparatus and makes measurement easy but also provides high precision and is suitable over a wide measurement range. The impact of the red and green color blocks, the lighting, and the direction of the surface texture on the correlation between the color index and the roughness are also assessed and discussed in this paper.

© 2016 Optical Society of America

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References

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    [Crossref]
  29. D. Sen and S. K. Pal, “Generalized Rough Sets, Entropy, and Image Ambiguity Measures,” IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 117–128 (2009).
    [Crossref] [PubMed]
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    [Crossref] [PubMed]
  34. O. V. Angelsky, S. G. Hanson, P. P. Polyanskii, A. P. Polyanskii, and A. L. Negrych, “Experimental demonstration of singular-optical colouring of regularly scattered white light,” J. Eur. Opt. Soc.-Rapid. 3(08029), 1–7 (2008).

2015 (2)

B. Kim and J. Seo, “Measurement of surface roughness of plasma-deposited films using laser speckles,” Appl. Surf. Sci. 359, 204–208 (2015).
[Crossref]

G. L. Grinblat, L. C. Uzal, P. F. Verdes, and P. M. Granitto, “Nonstationary regression with support vector machines,” Neural Comput. Appl. 26(3), 641–649 (2015).
[Crossref]

2014 (2)

S. Nammi and B. Ramamoorthy, “Effect of surface lay in the surface roughness evaluation using machine vision,” Optik (Stuttg.) 125(15), 3954–3960 (2014).
[Crossref]

G. Samtas, “Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network,” Int. J. Adv. Manuf. Technol. 73(1-4), 353–364 (2014).
[Crossref]

2013 (2)

W. Liu, X. M. Tu, Z. Y. Jia, W. Q. Wang, X. Ma, and X. D. Bi, “An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine,” Int. J. Adv. Manuf. Technol. 69(1-4), 583–593 (2013).
[Crossref]

R. Kamguem, S. A. Tahan, and V. Songmene, “Evaluation of machined part surface roughness using image texture gradient factor,” Int. J. Precis. Eng. Manuf. 14(2), 183–190 (2013).
[Crossref]

2012 (2)

A. Kolaman and O. Yadid-Pecht, “Quaternion structural similarity: a new quality index for color images,” IEEE Trans. Image Process. 21(4), 1526–1536 (2012).
[Crossref] [PubMed]

L. Sun, J. C. Xu, and Y. Tian, “Feature selection using rough entropy-based uncertainty measures in incomplete decision systems,” Knowl. Base. Syst. 36, 206–216 (2012).
[Crossref]

2011 (3)

S. Palani and U. Natarajan, “Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform,” Int. J. Adv. Manuf. Technol. 54(9-12), 1033–1042 (2011).
[Crossref]

R. P. Guo and Z. S. Tao, “Experimental investigation of a modified Beckmann-Kirchhoff scattering theory for the in-process optical measurement of surface quality,” Optik (Stuttg.) 122(21), 1890–1894 (2011).
[Crossref]

A. Murugarajan and G. L. Samuel, “Measurement, modeling and evaluation of surface parameter using capacitive-sensor-based measurement system,” Metrol. Meas. Syst. XVIII, 403–418 (2011).

2009 (1)

D. Sen and S. K. Pal, “Generalized Rough Sets, Entropy, and Image Ambiguity Measures,” IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 117–128 (2009).
[Crossref] [PubMed]

2008 (2)

S. P. Zhu, J. C. Fang, R. Zhou, J. H. Zhao, and W. B. Yu, “A new noncontact flatness measuring system of large 2-D flat workpiece,” IEEE Trans. Instrum. Meas. 57(12), 2891–2904 (2008).
[Crossref]

O. V. Angelsky, S. G. Hanson, P. P. Polyanskii, A. P. Polyanskii, and A. L. Negrych, “Experimental demonstration of singular-optical colouring of regularly scattered white light,” J. Eur. Opt. Soc.-Rapid. 3(08029), 1–7 (2008).

2007 (3)

G. A. Al-Kindi and B. Shirinzadeh, “An evaluation of surface roughness parameters measurement using vsion-based data,” Int. J. Mach. Tools Manuf. 47(3-4), 697–708 (2007).
[Crossref]

H. K. Chang, J. H. Kim, I. H. Kim, D. Y. Jang, and D. C. Han, “In-process surface roughness prediction using displacement signals from spindle motion,” Int. J. Mach. Tools Manuf. 47(6), 1021–1026 (2007).
[Crossref]

P. Priya and B. Ramamoorthy, “The influence of component inclination on surface finish evaluation using digital image processing,” Int. J. Mach. Tools Manuf. 47(3-4), 570–579 (2007).
[Crossref]

2006 (3)

2005 (3)

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

A. S. Rawat, N. O. Kawade, and A. K. Sinha, “Laser based surface roughness measuring instrument,” BARC report. 4, 1–30 (2005).

R. Kumar, P. Kulashekar, B. Dhanasekar, and B. Ramamoorthy, “Application of digital image magnification for surface roughness evaluation using machine vision,” Int. J. Mach. Tools Manuf. 45(2), 228–234 (2005).
[Crossref]

2004 (1)

E. S. Gadelmawla, “A vision system for surface roughness characterization using the gray level co-occurrence matrix,” NDT Int. 37(7), 577–588 (2004).
[Crossref]

2001 (1)

B. Y. Lee and Y. S. Tarng, “Surface roughness inspection by computer vision in turning operations,” Int. J. Mach. Tools Manuf. 41(9), 1251–1263 (2001).
[Crossref]

1999 (2)

Z. Yilbas and M. S. J. Hasmi, “Surface roughness measurement using an optical system,” J. Mater. Process. Technol. 88(1-3), 10–22 (1999).
[Crossref]

A. M. Nakamura, A. Kamei, M. Kogachi, and T. Mukai, “Laboratory measurements of laser-scattered light by rough surfaces,” Adv. Space Res. 23(7), 1201–1204 (1999).
[Crossref]

1998 (2)

M. A. Younis, “On line surface roughness measurements using image processing towards an adaptive control,” Comput. Ind. Eng. 35(1-2), 49–52 (1998).
[Crossref]

D. M. Tsai, J. J. Chen, and J. F. Chen, “A vision system for surface roughness assessment using neural networks,” Int. J. Adv. Manuf. Technol. 14(6), 412–422 (1998).
[Crossref]

1993 (1)

B. Ramamoorthy and V. Radhakrishnan, “Statistical approaches to surface texture classification,” Wear 167(2), 155–161 (1993).
[Crossref]

1989 (1)

F. Luk, V. Huynh, and W. North, “Measurement of surface roughness by a machine vision system,” J. Phys. E Sci. Instrum. 22(12), 977–980 (1989).
[Crossref]

Al-Kindi, G. A.

G. A. Al-Kindi and B. Shirinzadeh, “An evaluation of surface roughness parameters measurement using vsion-based data,” Int. J. Mach. Tools Manuf. 47(3-4), 697–708 (2007).
[Crossref]

Angelsky, O. V.

Bi, X. D.

W. Liu, X. M. Tu, Z. Y. Jia, W. Q. Wang, X. Ma, and X. D. Bi, “An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine,” Int. J. Adv. Manuf. Technol. 69(1-4), 583–593 (2013).
[Crossref]

Chang, H. K.

H. K. Chang, J. H. Kim, I. H. Kim, D. Y. Jang, and D. C. Han, “In-process surface roughness prediction using displacement signals from spindle motion,” Int. J. Mach. Tools Manuf. 47(6), 1021–1026 (2007).
[Crossref]

Chen, J. F.

D. M. Tsai, J. J. Chen, and J. F. Chen, “A vision system for surface roughness assessment using neural networks,” Int. J. Adv. Manuf. Technol. 14(6), 412–422 (1998).
[Crossref]

Chen, J. J.

D. M. Tsai, J. J. Chen, and J. F. Chen, “A vision system for surface roughness assessment using neural networks,” Int. J. Adv. Manuf. Technol. 14(6), 412–422 (1998).
[Crossref]

Deb, S.

X. S. Yang and S. Deb, “Cuckoo search via l’evy flights,”in World Congress on Nature & Biologically Inspired Computing (NaBIC, 2009), pp.210–214.
[Crossref]

Dhanasekar, B.

R. Kumar, P. Kulashekar, B. Dhanasekar, and B. Ramamoorthy, “Application of digital image magnification for surface roughness evaluation using machine vision,” Int. J. Mach. Tools Manuf. 45(2), 228–234 (2005).
[Crossref]

Fang, J. C.

S. P. Zhu, J. C. Fang, R. Zhou, J. H. Zhao, and W. B. Yu, “A new noncontact flatness measuring system of large 2-D flat workpiece,” IEEE Trans. Instrum. Meas. 57(12), 2891–2904 (2008).
[Crossref]

Gadelmawla, E. S.

E. S. Gadelmawla, “A vision system for surface roughness characterization using the gray level co-occurrence matrix,” NDT Int. 37(7), 577–588 (2004).
[Crossref]

Gledhill, D.

Goel, P. K.

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

Granitto, P. M.

G. L. Grinblat, L. C. Uzal, P. F. Verdes, and P. M. Granitto, “Nonstationary regression with support vector machines,” Neural Comput. Appl. 26(3), 641–649 (2015).
[Crossref]

Grinblat, G. L.

G. L. Grinblat, L. C. Uzal, P. F. Verdes, and P. M. Granitto, “Nonstationary regression with support vector machines,” Neural Comput. Appl. 26(3), 641–649 (2015).
[Crossref]

Guo, R. P.

R. P. Guo and Z. S. Tao, “Experimental investigation of a modified Beckmann-Kirchhoff scattering theory for the in-process optical measurement of surface quality,” Optik (Stuttg.) 122(21), 1890–1894 (2011).
[Crossref]

Han, D. C.

H. K. Chang, J. H. Kim, I. H. Kim, D. Y. Jang, and D. C. Han, “In-process surface roughness prediction using displacement signals from spindle motion,” Int. J. Mach. Tools Manuf. 47(6), 1021–1026 (2007).
[Crossref]

Hanson, S. G.

Hasmi, M. S. J.

Z. Yilbas and M. S. J. Hasmi, “Surface roughness measurement using an optical system,” J. Mater. Process. Technol. 88(1-3), 10–22 (1999).
[Crossref]

Huynh, V.

F. Luk, V. Huynh, and W. North, “Measurement of surface roughness by a machine vision system,” J. Phys. E Sci. Instrum. 22(12), 977–980 (1989).
[Crossref]

Jang, D. Y.

H. K. Chang, J. H. Kim, I. H. Kim, D. Y. Jang, and D. C. Han, “In-process surface roughness prediction using displacement signals from spindle motion,” Int. J. Mach. Tools Manuf. 47(6), 1021–1026 (2007).
[Crossref]

Jia, Z. Y.

W. Liu, X. M. Tu, Z. Y. Jia, W. Q. Wang, X. Ma, and X. D. Bi, “An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine,” Int. J. Adv. Manuf. Technol. 69(1-4), 583–593 (2013).
[Crossref]

Kamei, A.

A. M. Nakamura, A. Kamei, M. Kogachi, and T. Mukai, “Laboratory measurements of laser-scattered light by rough surfaces,” Adv. Space Res. 23(7), 1201–1204 (1999).
[Crossref]

Kamguem, R.

R. Kamguem, S. A. Tahan, and V. Songmene, “Evaluation of machined part surface roughness using image texture gradient factor,” Int. J. Precis. Eng. Manuf. 14(2), 183–190 (2013).
[Crossref]

Karimi, Y.

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

Kawade, N. O.

A. S. Rawat, N. O. Kawade, and A. K. Sinha, “Laser based surface roughness measuring instrument,” BARC report. 4, 1–30 (2005).

Kim, B.

B. Kim and J. Seo, “Measurement of surface roughness of plasma-deposited films using laser speckles,” Appl. Surf. Sci. 359, 204–208 (2015).
[Crossref]

Kim, I. H.

H. K. Chang, J. H. Kim, I. H. Kim, D. Y. Jang, and D. C. Han, “In-process surface roughness prediction using displacement signals from spindle motion,” Int. J. Mach. Tools Manuf. 47(6), 1021–1026 (2007).
[Crossref]

Kim, J. H.

H. K. Chang, J. H. Kim, I. H. Kim, D. Y. Jang, and D. C. Han, “In-process surface roughness prediction using displacement signals from spindle motion,” Int. J. Mach. Tools Manuf. 47(6), 1021–1026 (2007).
[Crossref]

Kogachi, M.

A. M. Nakamura, A. Kamei, M. Kogachi, and T. Mukai, “Laboratory measurements of laser-scattered light by rough surfaces,” Adv. Space Res. 23(7), 1201–1204 (1999).
[Crossref]

Kolaman, A.

A. Kolaman and O. Yadid-Pecht, “Quaternion structural similarity: a new quality index for color images,” IEEE Trans. Image Process. 21(4), 1526–1536 (2012).
[Crossref] [PubMed]

Kulashekar, P.

R. Kumar, P. Kulashekar, B. Dhanasekar, and B. Ramamoorthy, “Application of digital image magnification for surface roughness evaluation using machine vision,” Int. J. Mach. Tools Manuf. 45(2), 228–234 (2005).
[Crossref]

Kumar, R.

R. Kumar, P. Kulashekar, B. Dhanasekar, and B. Ramamoorthy, “Application of digital image magnification for surface roughness evaluation using machine vision,” Int. J. Mach. Tools Manuf. 45(2), 228–234 (2005).
[Crossref]

Lacroix, R.

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

Lee, B. Y.

B. Y. Lee and Y. S. Tarng, “Surface roughness inspection by computer vision in turning operations,” Int. J. Mach. Tools Manuf. 41(9), 1251–1263 (2001).
[Crossref]

Liu, W.

W. Liu, X. M. Tu, Z. Y. Jia, W. Q. Wang, X. Ma, and X. D. Bi, “An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine,” Int. J. Adv. Manuf. Technol. 69(1-4), 583–593 (2013).
[Crossref]

Lu, R. S.

Luk, F.

F. Luk, V. Huynh, and W. North, “Measurement of surface roughness by a machine vision system,” J. Phys. E Sci. Instrum. 22(12), 977–980 (1989).
[Crossref]

Ma, X.

W. Liu, X. M. Tu, Z. Y. Jia, W. Q. Wang, X. Ma, and X. D. Bi, “An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine,” Int. J. Adv. Manuf. Technol. 69(1-4), 583–593 (2013).
[Crossref]

Maksimyak, A. P.

Maksimyak, P. P.

Mukai, T.

A. M. Nakamura, A. Kamei, M. Kogachi, and T. Mukai, “Laboratory measurements of laser-scattered light by rough surfaces,” Adv. Space Res. 23(7), 1201–1204 (1999).
[Crossref]

Murugarajan, A.

A. Murugarajan and G. L. Samuel, “Measurement, modeling and evaluation of surface parameter using capacitive-sensor-based measurement system,” Metrol. Meas. Syst. XVIII, 403–418 (2011).

Nakamura, A. M.

A. M. Nakamura, A. Kamei, M. Kogachi, and T. Mukai, “Laboratory measurements of laser-scattered light by rough surfaces,” Adv. Space Res. 23(7), 1201–1204 (1999).
[Crossref]

Nammi, S.

S. Nammi and B. Ramamoorthy, “Effect of surface lay in the surface roughness evaluation using machine vision,” Optik (Stuttg.) 125(15), 3954–3960 (2014).
[Crossref]

Natarajan, U.

S. Palani and U. Natarajan, “Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform,” Int. J. Adv. Manuf. Technol. 54(9-12), 1033–1042 (2011).
[Crossref]

Negrych, A. L.

O. V. Angelsky, S. G. Hanson, P. P. Polyanskii, A. P. Polyanskii, and A. L. Negrych, “Experimental demonstration of singular-optical colouring of regularly scattered white light,” J. Eur. Opt. Soc.-Rapid. 3(08029), 1–7 (2008).

North, W.

F. Luk, V. Huynh, and W. North, “Measurement of surface roughness by a machine vision system,” J. Phys. E Sci. Instrum. 22(12), 977–980 (1989).
[Crossref]

Pal, S. K.

D. Sen and S. K. Pal, “Generalized Rough Sets, Entropy, and Image Ambiguity Measures,” IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 117–128 (2009).
[Crossref] [PubMed]

Palani, S.

S. Palani and U. Natarajan, “Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform,” Int. J. Adv. Manuf. Technol. 54(9-12), 1033–1042 (2011).
[Crossref]

Patel, R. M.

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

Polyanskii, A. P.

O. V. Angelsky, S. G. Hanson, P. P. Polyanskii, A. P. Polyanskii, and A. L. Negrych, “Experimental demonstration of singular-optical colouring of regularly scattered white light,” J. Eur. Opt. Soc.-Rapid. 3(08029), 1–7 (2008).

Polyanskii, P. P.

O. V. Angelsky, S. G. Hanson, P. P. Polyanskii, A. P. Polyanskii, and A. L. Negrych, “Experimental demonstration of singular-optical colouring of regularly scattered white light,” J. Eur. Opt. Soc.-Rapid. 3(08029), 1–7 (2008).

Polyanskii, P. V.

Prasher, S. O.

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

Priya, P.

P. Priya and B. Ramamoorthy, “The influence of component inclination on surface finish evaluation using digital image processing,” Int. J. Mach. Tools Manuf. 47(3-4), 570–579 (2007).
[Crossref]

Radhakrishnan, V.

B. Ramamoorthy and V. Radhakrishnan, “Statistical approaches to surface texture classification,” Wear 167(2), 155–161 (1993).
[Crossref]

Ramamoorthy, B.

S. Nammi and B. Ramamoorthy, “Effect of surface lay in the surface roughness evaluation using machine vision,” Optik (Stuttg.) 125(15), 3954–3960 (2014).
[Crossref]

P. Priya and B. Ramamoorthy, “The influence of component inclination on surface finish evaluation using digital image processing,” Int. J. Mach. Tools Manuf. 47(3-4), 570–579 (2007).
[Crossref]

R. Kumar, P. Kulashekar, B. Dhanasekar, and B. Ramamoorthy, “Application of digital image magnification for surface roughness evaluation using machine vision,” Int. J. Mach. Tools Manuf. 45(2), 228–234 (2005).
[Crossref]

B. Ramamoorthy and V. Radhakrishnan, “Statistical approaches to surface texture classification,” Wear 167(2), 155–161 (1993).
[Crossref]

Rawat, A. S.

A. S. Rawat, N. O. Kawade, and A. K. Sinha, “Laser based surface roughness measuring instrument,” BARC report. 4, 1–30 (2005).

Samtas, G.

G. Samtas, “Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network,” Int. J. Adv. Manuf. Technol. 73(1-4), 353–364 (2014).
[Crossref]

Samuel, G. L.

A. Murugarajan and G. L. Samuel, “Measurement, modeling and evaluation of surface parameter using capacitive-sensor-based measurement system,” Metrol. Meas. Syst. XVIII, 403–418 (2011).

Sen, D.

D. Sen and S. K. Pal, “Generalized Rough Sets, Entropy, and Image Ambiguity Measures,” IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 117–128 (2009).
[Crossref] [PubMed]

Seo, J.

B. Kim and J. Seo, “Measurement of surface roughness of plasma-deposited films using laser speckles,” Appl. Surf. Sci. 359, 204–208 (2015).
[Crossref]

Shirinzadeh, B.

G. A. Al-Kindi and B. Shirinzadeh, “An evaluation of surface roughness parameters measurement using vsion-based data,” Int. J. Mach. Tools Manuf. 47(3-4), 697–708 (2007).
[Crossref]

Sinha, A. K.

A. S. Rawat, N. O. Kawade, and A. K. Sinha, “Laser based surface roughness measuring instrument,” BARC report. 4, 1–30 (2005).

Songmene, V.

R. Kamguem, S. A. Tahan, and V. Songmene, “Evaluation of machined part surface roughness using image texture gradient factor,” Int. J. Precis. Eng. Manuf. 14(2), 183–190 (2013).
[Crossref]

Sun, L.

L. Sun, J. C. Xu, and Y. Tian, “Feature selection using rough entropy-based uncertainty measures in incomplete decision systems,” Knowl. Base. Syst. 36, 206–216 (2012).
[Crossref]

Tahan, S. A.

R. Kamguem, S. A. Tahan, and V. Songmene, “Evaluation of machined part surface roughness using image texture gradient factor,” Int. J. Precis. Eng. Manuf. 14(2), 183–190 (2013).
[Crossref]

Tao, Z. S.

R. P. Guo and Z. S. Tao, “Experimental investigation of a modified Beckmann-Kirchhoff scattering theory for the in-process optical measurement of surface quality,” Optik (Stuttg.) 122(21), 1890–1894 (2011).
[Crossref]

Tarng, Y. S.

B. Y. Lee and Y. S. Tarng, “Surface roughness inspection by computer vision in turning operations,” Int. J. Mach. Tools Manuf. 41(9), 1251–1263 (2001).
[Crossref]

Tian, G. Y.

Tian, Y.

L. Sun, J. C. Xu, and Y. Tian, “Feature selection using rough entropy-based uncertainty measures in incomplete decision systems,” Knowl. Base. Syst. 36, 206–216 (2012).
[Crossref]

Tsai, D. M.

D. M. Tsai, J. J. Chen, and J. F. Chen, “A vision system for surface roughness assessment using neural networks,” Int. J. Adv. Manuf. Technol. 14(6), 412–422 (1998).
[Crossref]

Tu, X. M.

W. Liu, X. M. Tu, Z. Y. Jia, W. Q. Wang, X. Ma, and X. D. Bi, “An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine,” Int. J. Adv. Manuf. Technol. 69(1-4), 583–593 (2013).
[Crossref]

Uno, Y.

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

Uzal, L. C.

G. L. Grinblat, L. C. Uzal, P. F. Verdes, and P. M. Granitto, “Nonstationary regression with support vector machines,” Neural Comput. Appl. 26(3), 641–649 (2015).
[Crossref]

Verdes, P. F.

G. L. Grinblat, L. C. Uzal, P. F. Verdes, and P. M. Granitto, “Nonstationary regression with support vector machines,” Neural Comput. Appl. 26(3), 641–649 (2015).
[Crossref]

Viau, A.

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

Wang, W. Q.

W. Liu, X. M. Tu, Z. Y. Jia, W. Q. Wang, X. Ma, and X. D. Bi, “An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine,” Int. J. Adv. Manuf. Technol. 69(1-4), 583–593 (2013).
[Crossref]

Ward, S.

Xu, J. C.

L. Sun, J. C. Xu, and Y. Tian, “Feature selection using rough entropy-based uncertainty measures in incomplete decision systems,” Knowl. Base. Syst. 36, 206–216 (2012).
[Crossref]

Yadid-Pecht, O.

A. Kolaman and O. Yadid-Pecht, “Quaternion structural similarity: a new quality index for color images,” IEEE Trans. Image Process. 21(4), 1526–1536 (2012).
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Yang, X. S.

X. S. Yang and S. Deb, “Cuckoo search via l’evy flights,”in World Congress on Nature & Biologically Inspired Computing (NaBIC, 2009), pp.210–214.
[Crossref]

Yilbas, Z.

Z. Yilbas and M. S. J. Hasmi, “Surface roughness measurement using an optical system,” J. Mater. Process. Technol. 88(1-3), 10–22 (1999).
[Crossref]

Younis, M. A.

M. A. Younis, “On line surface roughness measurements using image processing towards an adaptive control,” Comput. Ind. Eng. 35(1-2), 49–52 (1998).
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Yu, W. B.

S. P. Zhu, J. C. Fang, R. Zhou, J. H. Zhao, and W. B. Yu, “A new noncontact flatness measuring system of large 2-D flat workpiece,” IEEE Trans. Instrum. Meas. 57(12), 2891–2904 (2008).
[Crossref]

Zhao, J. H.

S. P. Zhu, J. C. Fang, R. Zhou, J. H. Zhao, and W. B. Yu, “A new noncontact flatness measuring system of large 2-D flat workpiece,” IEEE Trans. Instrum. Meas. 57(12), 2891–2904 (2008).
[Crossref]

Zhou, R.

S. P. Zhu, J. C. Fang, R. Zhou, J. H. Zhao, and W. B. Yu, “A new noncontact flatness measuring system of large 2-D flat workpiece,” IEEE Trans. Instrum. Meas. 57(12), 2891–2904 (2008).
[Crossref]

Zhu, S. P.

S. P. Zhu, J. C. Fang, R. Zhou, J. H. Zhao, and W. B. Yu, “A new noncontact flatness measuring system of large 2-D flat workpiece,” IEEE Trans. Instrum. Meas. 57(12), 2891–2904 (2008).
[Crossref]

Adv. Space Res. (1)

A. M. Nakamura, A. Kamei, M. Kogachi, and T. Mukai, “Laboratory measurements of laser-scattered light by rough surfaces,” Adv. Space Res. 23(7), 1201–1204 (1999).
[Crossref]

Appl. Opt. (1)

Appl. Surf. Sci. (1)

B. Kim and J. Seo, “Measurement of surface roughness of plasma-deposited films using laser speckles,” Appl. Surf. Sci. 359, 204–208 (2015).
[Crossref]

BARC report. (1)

A. S. Rawat, N. O. Kawade, and A. K. Sinha, “Laser based surface roughness measuring instrument,” BARC report. 4, 1–30 (2005).

Comput. Electron. Agric. (1)

Y. Uno, S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data,” Comput. Electron. Agric. 47(2), 149–161 (2005).
[Crossref]

Comput. Ind. Eng. (1)

M. A. Younis, “On line surface roughness measurements using image processing towards an adaptive control,” Comput. Ind. Eng. 35(1-2), 49–52 (1998).
[Crossref]

IEEE Trans. Image Process. (1)

A. Kolaman and O. Yadid-Pecht, “Quaternion structural similarity: a new quality index for color images,” IEEE Trans. Image Process. 21(4), 1526–1536 (2012).
[Crossref] [PubMed]

IEEE Trans. Instrum. Meas. (1)

S. P. Zhu, J. C. Fang, R. Zhou, J. H. Zhao, and W. B. Yu, “A new noncontact flatness measuring system of large 2-D flat workpiece,” IEEE Trans. Instrum. Meas. 57(12), 2891–2904 (2008).
[Crossref]

IEEE Trans. Syst. Man Cybern. B Cybern. (1)

D. Sen and S. K. Pal, “Generalized Rough Sets, Entropy, and Image Ambiguity Measures,” IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 117–128 (2009).
[Crossref] [PubMed]

Int. J. Adv. Manuf. Technol. (4)

G. Samtas, “Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network,” Int. J. Adv. Manuf. Technol. 73(1-4), 353–364 (2014).
[Crossref]

W. Liu, X. M. Tu, Z. Y. Jia, W. Q. Wang, X. Ma, and X. D. Bi, “An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine,” Int. J. Adv. Manuf. Technol. 69(1-4), 583–593 (2013).
[Crossref]

S. Palani and U. Natarajan, “Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform,” Int. J. Adv. Manuf. Technol. 54(9-12), 1033–1042 (2011).
[Crossref]

D. M. Tsai, J. J. Chen, and J. F. Chen, “A vision system for surface roughness assessment using neural networks,” Int. J. Adv. Manuf. Technol. 14(6), 412–422 (1998).
[Crossref]

Int. J. Mach. Tools Manuf. (5)

R. Kumar, P. Kulashekar, B. Dhanasekar, and B. Ramamoorthy, “Application of digital image magnification for surface roughness evaluation using machine vision,” Int. J. Mach. Tools Manuf. 45(2), 228–234 (2005).
[Crossref]

B. Y. Lee and Y. S. Tarng, “Surface roughness inspection by computer vision in turning operations,” Int. J. Mach. Tools Manuf. 41(9), 1251–1263 (2001).
[Crossref]

P. Priya and B. Ramamoorthy, “The influence of component inclination on surface finish evaluation using digital image processing,” Int. J. Mach. Tools Manuf. 47(3-4), 570–579 (2007).
[Crossref]

H. K. Chang, J. H. Kim, I. H. Kim, D. Y. Jang, and D. C. Han, “In-process surface roughness prediction using displacement signals from spindle motion,” Int. J. Mach. Tools Manuf. 47(6), 1021–1026 (2007).
[Crossref]

G. A. Al-Kindi and B. Shirinzadeh, “An evaluation of surface roughness parameters measurement using vsion-based data,” Int. J. Mach. Tools Manuf. 47(3-4), 697–708 (2007).
[Crossref]

Int. J. Precis. Eng. Manuf. (1)

R. Kamguem, S. A. Tahan, and V. Songmene, “Evaluation of machined part surface roughness using image texture gradient factor,” Int. J. Precis. Eng. Manuf. 14(2), 183–190 (2013).
[Crossref]

J. Eur. Opt. Soc.-Rapid. (1)

O. V. Angelsky, S. G. Hanson, P. P. Polyanskii, A. P. Polyanskii, and A. L. Negrych, “Experimental demonstration of singular-optical colouring of regularly scattered white light,” J. Eur. Opt. Soc.-Rapid. 3(08029), 1–7 (2008).

J. Mater. Process. Technol. (1)

Z. Yilbas and M. S. J. Hasmi, “Surface roughness measurement using an optical system,” J. Mater. Process. Technol. 88(1-3), 10–22 (1999).
[Crossref]

J. Phys. E Sci. Instrum. (1)

F. Luk, V. Huynh, and W. North, “Measurement of surface roughness by a machine vision system,” J. Phys. E Sci. Instrum. 22(12), 977–980 (1989).
[Crossref]

Knowl. Base. Syst. (1)

L. Sun, J. C. Xu, and Y. Tian, “Feature selection using rough entropy-based uncertainty measures in incomplete decision systems,” Knowl. Base. Syst. 36, 206–216 (2012).
[Crossref]

Metrol. Meas. Syst. (1)

A. Murugarajan and G. L. Samuel, “Measurement, modeling and evaluation of surface parameter using capacitive-sensor-based measurement system,” Metrol. Meas. Syst. XVIII, 403–418 (2011).

NDT Int. (1)

E. S. Gadelmawla, “A vision system for surface roughness characterization using the gray level co-occurrence matrix,” NDT Int. 37(7), 577–588 (2004).
[Crossref]

Neural Comput. Appl. (1)

G. L. Grinblat, L. C. Uzal, P. F. Verdes, and P. M. Granitto, “Nonstationary regression with support vector machines,” Neural Comput. Appl. 26(3), 641–649 (2015).
[Crossref]

Opt. Express (2)

Optik (Stuttg.) (2)

R. P. Guo and Z. S. Tao, “Experimental investigation of a modified Beckmann-Kirchhoff scattering theory for the in-process optical measurement of surface quality,” Optik (Stuttg.) 122(21), 1890–1894 (2011).
[Crossref]

S. Nammi and B. Ramamoorthy, “Effect of surface lay in the surface roughness evaluation using machine vision,” Optik (Stuttg.) 125(15), 3954–3960 (2014).
[Crossref]

Wear (1)

B. Ramamoorthy and V. Radhakrishnan, “Statistical approaches to surface texture classification,” Wear 167(2), 155–161 (1993).
[Crossref]

Other (3)

R. D. Dony and S. Wesolkowski, “Edge detection on color images using rgb vector angles,” in Electrical and Computer Engineering in Canadian Conference (IEEE, 1999), pp. 687–692.
[Crossref]

M. A. Sutton, J. J. Orteu, and H. Schreier, Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications (Springer Science & Business Media, 2009).

X. S. Yang and S. Deb, “Cuckoo search via l’evy flights,”in World Congress on Nature & Biologically Inspired Computing (NaBIC, 2009), pp.210–214.
[Crossref]

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Figures (13)

Fig. 1
Fig. 1 Diagram of rough surface imaging. (a) Impact of a rough surface on imaging. (b) Vertical impact of a rough surface on imaging.
Fig. 2
Fig. 2 Target object and its virtual image on (a) low-roughness surface and (b) high-roughness surface.
Fig. 3
Fig. 3 Experimental model.
Fig. 4
Fig. 4 Machine vision-based roughness measurement setup.
Fig. 5
Fig. 5 Color block design. From left to right, the number of color block is two, four, 64 and 256.
Fig. 6
Fig. 6 Experimental procedure flow chart
Fig. 7
Fig. 7 Images before (a) contamination and (b) after contamination.
Fig. 8
Fig. 8 Curves relating each index to the roughness of (a) clean surface and (b) contaminated surface.
Fig. 9
Fig. 9 Raw data and regression data. Left: Ga index. Right: CD index.
Fig. 10
Fig. 10 Color index versus roughness.
Fig. 11
Fig. 11 Impact of the texture orientation of the grinding sample on CD (brightness of 110) when the color block contains sub-blocks of (a) two, (b) four, (c) 64 and (d) 256 respectively.
Fig. 12
Fig. 12 Relationship between lighting and CD. Surface texture is (a) perpendicular and (b) horizontal to the workbench respectively when the number of color block is two. Surface texture is (c) perpendicular and (d) horizontal to the workbench respectively when the number of color block is 256.
Fig. 13
Fig. 13 CD measurement area with a roughness of (a) 0.052 μm and (b) 0.826 μm. (c) Color division model. (d) Color difference for the 0.052 μm sample (3D plot).

Tables (5)

Tables Icon

Table 1 Roughnesses of the 15 Test Samples (Unit: μm)

Tables Icon

Table 2 R2 Values of the Curves Fitted to the Four Indices before and after the Sample is Contaminated

Tables Icon

Table 3 Prediction Results and Errors Rate of Four Indices

Tables Icon

Table 4 Prediction Results and Error Rates for the Four Color Block Designs

Tables Icon

Table 5 Nine Roughness Measurement (Units: μm) Made Using a Stylus-based Method

Equations (18)

Equations on this page are rendered with MathJax. Learn more.

I(i,j)=f(L(i,j),F(i,j),R(i,j))
F(x,y)=R(x,y) r +G(x,y) g +B(x,y) b
R A1(i,j) E A r A1 2 R A2(i,j) E A r A2 2
G B1(i,j) E B r B1 2 G B2(i,j) E B r B2 2
G A1(i,j) = R B1(i,j) =0
L (A2B2)(i,j) = R A2(i,j) + G B2(i,j) E A r A2 2 + E B r B2 2
| R A1(i,j) G A1(i,j) || R A2(i,j) G A2(i,j) |
| G B1(i,j) R B1(i,j) || G B2(i,j) R B2(i,j) |
CD= 1 M×N i=1 M j=1 N | R (i,j) G (i,j) |
Ra=f(CD)
Ga= 1 M×N i=1 M j=1 N |I(i,j) I m )|
F2=λ
En= i=1 m j=1 n I(i,j)In[ I(i,j) ]
C D normal (i)=CD(i)/max(CD)
F 2 (i)=1F2(i)/max(F2)
F 2 normal (i)=F 2 (i)+(1max(F 2 ))
R 2 =1 SSE SST =1 (yy) 2 (y y ¯ ) 2
δ= | predictionstylus measured value | stylus measured value ×100

Metrics