CLC number: TP2; TP391.41
On-line Access:
Received: 2009-03-12
Revision Accepted: 2009-06-24
Crosschecked: 2010-01-05
Cited: 0
Clicked: 8247
Hong ZHOU, Hai-er XU, Pei-qi HE, Zhi-bai SONG, Chen-ge GENG. Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm[J]. Journal of Zhejiang University Science C, 2010, 11(3): 199-205.
@article{title="Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm",
author="Hong ZHOU, Hai-er XU, Pei-qi HE, Zhi-bai SONG, Chen-ge GENG",
journal="Journal of Zhejiang University Science C",
volume="11",
number="3",
pages="199-205",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C0910144"
}
%0 Journal Article
%T Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm
%A Hong ZHOU
%A Hai-er XU
%A Pei-qi HE
%A Zhi-bai SONG
%A Chen-ge GENG
%J Journal of Zhejiang University SCIENCE C
%V 11
%N 3
%P 199-205
%@ 1869-1951
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910144
TY - JOUR
T1 - Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm
A1 - Hong ZHOU
A1 - Hai-er XU
A1 - Pei-qi HE
A1 - Zhi-bai SONG
A1 - Chen-ge GENG
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 3
SP - 199
EP - 205
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C0910144
Abstract: light emitting diode (LED) indicators used on automobile meters are essential for safe driving and few errors can be tolerated. The current manual inspection approach can achieve only 95% accuracy rate in weeding out errors occurring in the production process. It is imperative to improve the accuracy of the inspection process to better achieve the goal of safe driving. This paper proposes an automatic inspection method for LED indicators for use on automobile meters. Firstly, red-green-blue (RGB) color images of LED indicators are acquired and converted into R, G, and B intensity images. A seeded region growing (SRG) algorithm, which selects seeds automatically based on Otsu’s method, is then used to extract the LED indicator regions. Finally, a region matching process based on the seed and three area parameters of each region is applied to inspect the LED indicators one by one to locate any errors. Experiments on standard automobile meters showed that the inspection accuracy rate of this method was up to 99.52% and the inspection speed was faster compared with the manual method. Thus, the new method shows good prospects for practical application.
[1] Adamek, T., O′Connor, N., Murphy, N., 2005. Region-Based Segmentation of Images Using Syntactic Visual Features. 6th Int. Workshop Image Analysis for Multimedia Interactive Services, p.1-4.
[2] Adams, R., Bischof, L., 1994. Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell., 16(6):641-647.
[3] Buxton, B.F., Abdallahi, H., Delmiro, F.R., Jarra, W., 2007. Development of an Extension of the Otsu Algorithm for Multidimensional Image Segmentation of Thin-Film Blood Slides. Proc. Int. Conf. on Computing: Theory and Applications, p.552-562.
[4] Chang, C.Y., Li, C.H., Lin, S.Y., Jeng, M., 2009. Application of two Hopfield neural networks for automatic four-element LED inspection. IEEE Trans. Syst. Man Cybern., 39(3):352-365.
[5] Ghazi Saeidi, R., Latifi, M., Shaikhzadeh Najar, S., Ghazi Saeidi, A., 2005. Computer vision-aided fabric inspection system for on-circular knitting machine. Textile Res. J., 75(6):492-497.
[6] Gonzalez, R.C., Woods, R.E., Eddins, S.L., 2003. Digital Image Processing Using MATLAB. Prentice Hall Inc., NJ, USA, p.195-241.
[7] Ikonomatakis, N., Plataniotis, K.N., Zervakis, M., Venetsanopoulos, A.N., 1997. Region Growing and Region Merging Image Segmentation. Proc. 13th Int. Conf. on Digital Signal Processing, p.299-302.
[8] Kumar, A., 2008. Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron., 55(1):348-363.
[9] Lee, B.Y., Tarng, Y.S., 2001. Surface roughness inspection by computer vision in turning operations. Int. J. Mach. Tools Manuf., 41(9):1251-1263.
[10] Malamas, E.N., Petrakis, E.G.M., Zervakis, M., Petit, L., Legat, J.D., 2003. A survey on industrial vision system, applications and tools. Image Vis. Comput., 21(2):171-188.
[11] Moganti, M., Ercal, F., Dagli, C.H., Tsunekawa, S., 1996. Automatic PCB inspection algorithms: a survey. Comput. Vis. Image Understand., 63(2):287-313.
[12] Newman, T.S., Jain, A.K., 1995. A survey of automated visual inspection. Comput. Vis. Image Understand., 61(2):231-262.
[13] Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 9(1):62-66.
[14] Pal, N.R., Pal, S.K., 1993. A review on image segmentation techniques. Pattern Recogn., 26(9):1277-1294.
[15] Perng, D.B., Chou, C.C., Chen, W.Y., 2007. A novel vision system for CRT panel auto-inspection. J. Chin. Inst. Ind. Eng., 24(5):341-350.
[16] Tuduki, Y., Murase, K., Izumida, M., Miki, H., Kikuchi, K., Murakami, K., Ikezoe, J., 2000. Automated Seeded Region Growing Algorithm for Extraction of Cerebral Blood Vessels from Magnetic Resonance Angiographic Data. Proc. 22nd Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 3:1756-1759.
[17] Wu, H.S., Barba, J., Gil, J., 1996. Region growing segmentation of textured cell image. Electron. Lett., 32(12):1084-1085.
[18] Wu, L.M., Wu, F.J., Wang, G.T., 2008. Computer Vision Inspection for IC Wafer Based on Character of Pixels Distribution. Proc. 3rd Int. Conf. on Convergence and Hybrid Information Technology, 2:248-251.
[19] Zhang, Y.J., 1996. A survey on evaluation methods for image segmentation. Pattern Recogn., 29(8):1335-1346.
Open peer comments: Debate/Discuss/Question/Opinion
<1>