Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2010.16.5.469

Metal pad Discolored Image Classification Algorithm using Geometric Texture Information  

Cui, Xue Nan (인하대학교 정보공학과)
Kim, Hak-Il (인하대학교 정보공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.16, no.5, 2010 , pp. 469-475 More about this Journal
Abstract
This paper presents a method of classifying discolored defects of metal pads using geometric texture for AFVI (Automated Final Vision Inspection) systems. In PCB manufacturing process, the metal pads on PCB can be oxidized and discolored partly due to various environmental factors. Nowadays the discolored defects are manually detected and rejected from the process. This paper proposes an efficient geometric texture feature, SUTF (Symmetry and Uniformity Texture Feature) based on the symmetric and uniform textural characteristics of the surface of circular metal pads for automating AFVI systems. In practical experiments with real samples acquired from a production line, 30 discolored images and 1232 roughness images are tested. The experimental results demonstrate that the proposed method using SUTFs provides better performance compared to Gabor feature with 0% FNR (False Negative Rate) and 1.46% FPR (False Positive Rate). The performance of the proposed method shows its applicability in the real manufacturing systems.
Keywords
geometric texture; symmetry; uniformity; discolored image; roughness;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By SCOPUS : 2
연도 인용수 순위
1 X. Zeng, Y. Chen, Z. Nakao, and H. Lu, “Texture representation based on pattern map,” Signal Process, vol. 84, no. 3, pp. 589-599, 3, 2004.   DOI
2 C. E. Honeycutt and R. Plotnick, “Image analysis techniques and gray-level co-occurrence matrices (GLCM) for calculating bioturbation indices and characterizing biogenic sedimentary structures,” Comput. Geosci., vol. 34, no. 11, pp. 1461-1472, Nov. 2008.   DOI
3 H. Zhou, R. Wang, and C. Wang, “A novel extended localbinary-pattern operator for texture analysis,” Information Sciences: an International Journal, vol. 178, no. 22, pp. 4314-4325, 11/15, 2008.   DOI   ScienceOn
4 J. Melendez, M. Garcia, and D. Puig, “Efficient distance-based per-pixel texture classification with gabor wavelet filters,” Pattern Analysis & Applications, vol. 11, no. 3, pp. 365-372, 2008.   DOI
5 S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on gabor filters,” IEEE Transactions on Image Processing, vol. 11, no. 10, pp. 1160-1167, Oct. 2002.   DOI
6 H. Lu, Y. Huang, Y. Chen, and D. yang, “Automatic gender recognition based on pixel-pattern-based texture feature,” Journal of Real-time Image Processing 3, vol. 3, no. 1-2, pp. 109-116, 2008.
7 H. F. Ng, “Automatic thresholding for defect detection,” Pattern Recognition Letters, vol. 27, pp. 1644-1649, 2006.   DOI
8 X. N. Cui, E. S. Park, J. C. Kim, and H. I. Kim, “Discolored metal pad image classification based on gabor texture features using GPU,” Journal of Institute of Control, Robotics and Systems, vol. 15, no. 8, pp. 778-785, August 2009.   과학기술학회마을   DOI