JSEMAT  Vol.2 No.2 , April 2012
A Fast Algorithm for Automated Quality Control in Surface Engineering
Abstract: In this article an approach to surface image quality assessment for surface pattern and object recognition, classification, and identification has been described. The surface quality assessment finds many industrial applications such as auto-mated, advanced, and autonomous manufacturing processes. Given that in most industrial applications the target surface is an unknown variable, having a tool to measure the quality of the surface in real time has a significant value. To add to the complication, in most industrial applications, the surface (and therefore its image) suffers from several physical phenomena such as noise (of several different kinds), time, phase, and frequency shifts, and other clutter caused by interference and speckles. The proposed tool should also be able to measure the level of deterioration of the surface due to these environmental effects. Therefore, evaluation of quality of a surface is not an easy task. It requires a good understanding of the processing methods used and the types of environmental processes affecting the surface. On the other hand, for a meaningful comparative analysis, some effective parameters have to be chosen and qualitatively and quantitatively measured across different settings and processes affecting the surface. Finally, any algorithm capable of handling these tasks has to be efficient, fast, and simple to qualify for the “real-time” applications.
Cite this paper: E. Sheybani, S. Garcia-Otero, F. Adnani and G. Javidi, "A Fast Algorithm for Automated Quality Control in Surface Engineering," Journal of Surface Engineered Materials and Advanced Technology, Vol. 2 No. 2, 2012, pp. 120-126. doi: 10.4236/jsemat.2012.22019.

[1]   J. D. Maxwell, Y. Qu and J. R. Howell, “Full Field Temperature Measurement of Specular Wafers during Rapid Thermal Processing,” IEEE Transactions on Semiconductor Manufacturing, Vol. 20, No. 2, 2007, pp. 137-142. doi:10.1109/TSM.2007.895204

[2]   K.-C. Huang; C.-L. Chang and W.-H. Wu, “Novel Image Polarization Method for Measurement of Lens Decentration,” IEEE Transactions on Instrumentation and Measurement, Vol. 60, No. 5, 2011, pp. 1845-1853. doi:10.1109/TIM.2011.2108070

[3]   Y. Cheng and M. A. Jafari, “Vision-Based Online Process Control in Manufacturing Applications,” IEEE Transactions on Automation Science and Engineering, Vol. 5, No. 1, 2008, pp. 140-153. doi:10.1109/TASE.2007.912058

[4]   D.-M. Tsai and J.-Y. Luo, “Mean Shift-Based Defect Detection in Multicrystalline Solar Wafer Surfaces,” IEEE Transactions on Industrial Informatics, Vol. 7, No. 1, 2011, pp. 125-135. doi:10.1109/TII.2010.2092783

[5]   F. Bernardini, I. M. Martin and H. Rushmeier, “High-Quality Texture Reconstruction from Multiple Scans,” IEEE Transactions on Visualization and Computer Graphics, Vol. 7, No. 4, 2001, pp. 318-332. doi:10.1109/2945.965346

[6]   J. P. W. Pluim, J. B. A. Maintz and M. A. Viergever, “Mutual-Information-Based Registration of Medical Images: A Survey,” IEEE Transactions on Medical Imaging, Vol. 22, No. 8, 2003, pp. 986-1004. doi:10.1109/TMI.2003.815867

[7]   S. Reed, I. T. Ruiz, C. Capus and Y. Petillot, “The Fusion of Large Scale Classified Side-Scan Sonar Image Mosaics,” IEEE Transactions on Image Processing, Vol. 15, No. 7, 2006, pp. 2049-2060. doi:10.1109/TIP.2006.873448

[8]   H. R. Sheikh and A. C. Bovik, “Image Information and Visual Quality,” IEEE Transactions on Image Processing, Vol. 15, No. 2, 2006, pp. 430-444. doi:10.1109/TIP.2005.859378

[9]   Z. Wang and A. C. Bovik, “A Universal Image Quality Index,” IEEE Signal Processing Letters, Vol. 9, No. 3, 2002, pp. 81-84. doi:10.1109/97.995823

[10]   R. Samadani, T. A. Mauer, D. M. Berfanger and J. H. Clark, “Image Thumbnails That Represent Blur and Noise,” IEEE Transactions on Image Processing, Vol. 19, No. 2, 2010, pp. 363-373. doi:10.1109/TIP.2009.2035847

[11]   M. Mignotte, “A Post-Processing Deconvolution Step for Wavelet-Based Image Denoising Methods,” IEEE Signal Processing Letters, Vol. 14, No. 9, 2007, pp. 621-624. doi:10.1109/LSP.2007.896183

[12]   V. Chappelier and C. Guillemot, “Oriented Wavelet Transform for Image Compression and Denoising,” IEEE Transactions on Image Processing, Vol. 15, No. 10, 2006, pp. 2892-2903. doi:10.1109/TIP.2006.877526

[13]   E. J. Balster, Y. F. Zheng and R. L. Ewing, “Feature- Based Wavelet Shrinkage Algorithm for Image Denoising,” IEEE Transactions on Image Processing, Vol. 14, No. 12, 2005, pp. 2024-2039. doi:10.1109/TIP.2005.859385

[14]   E. Sheybani, “Enhancement of Data in Integrated Communications, Navigation, and Surveillance Systems,” Proceedings of NASA/IEEE/AIAA ICNS 2011, Washington DC, 10-12 May 2011.

[15]   N. C. Pramod and G. V. Anand, “Nonlinear Wavelet Denoising for DOA Estimation by MUSIC,” 2004 International Conference on Signal Processing & Communications (SPCOM), Bangalore, 11-14 December 2004, pp. 388-392.

[16]   E. Davies, “Machine Vision: Theory, Algorithms and Practicalities,” Academic Press, New York, 1990, pp. 42- 44.

[17]   R. Gonzalez and R. Woods, “Digital Image Processing,” Addison-Wesley Publishing Com-pany, Reading, 1992, p. 191.

[18]   R. Haralick and L. Shapiro, “Computer and Robot Vision,” Addison-Wesley Publishing Company, Reading, 1992.

[19]   F. Jin, P. Fieguth, L. Winger and E. Jernigan, “Adaptive wiener filtering of noisy images and image sequences,” Proceedings of IEEE ICIP 2003, Barcelona, 14-17 September 2003.

[20]   LaserSoft Imaging, “SilverFast Unsharp Masking,” 2011.

[21]   D. Ziou and S. Tabbone, “Edge Detection Techniques: An Overview,” International Journal of Pattern Recognition and Image Analysis, Vol. 8, No. 4, 1998, pp. 537- 559.

[22]   J. Canny, “A Compu-tational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, 1986, pp. 679-714. doi:10.1109/TPAMI.1986.4767851

[23]   Wikipedia, “Signal-to-Noise Ratio,” 2011.

[24]   J. Stensby, “Notes on Matrix Norms,” 2011.