Browse > Article

The Development of Image Processing System Using Area Camera for Feeding Lumber  

Kim, Byung Nam (Division of Wood Engineering, Department of Forest Products, Korea Forest Research Institute)
Lee, Hyoung Woo (College of Agriculture & Life Science, Chonnam National University)
Kim, Kwang Mo (Division of Wood Engineering, Department of Forest Products, Korea Forest Research Institute)
Publication Information
Journal of the Korean Wood Science and Technology / v.37, no.1, 2009 , pp. 37-47 More about this Journal
Abstract
For the inspection of wood, machine vision is the most common automated inspection method used at present. It is required to sort wood products by grade and to locate surface defects prior to cut-up. Many different sensing methods have been applied to inspection of wood including optical, ultrasonic, X-ray sensing in the wood industry. Nowadays the scanning system mainly employs CCD line-scan camera to meet the needs of accurate detection of lumber defects and real-time image processing. But this system needs exact feeding system and low deviation of lumber thickness. In this study low cost CCD area sensor was used for the development of image processing system for lumber being fed. When domestic red pine being fed on the conveyer belt, lumber images of irregular term of captured area were acquired because belt conveyor slipped between belt and roller. To overcome incorrect image merging by the unstable feeding speed of belt conveyor, it was applied template matching algorithm which was a measure of the similarity between the pattern of current image and the next one. Feeding the lumber over 13.8 m/min, general area sensor generates unreadable image pattern by the motion blur. The red channel of RGB filter showed a good performance for removing background of the green conveyor belt from merged image. Threshold value reduction method that was a image-based thresholding algorithm performed well for knot detection.
Keywords
Image processing; CCD area sensor; image merging; thresholding; knot; lumber defects;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 McMillin, C. W., R. W. Conners, and H. A. Huber. 1984. ALPS-A potential new automated lumber processing system. Forest Products Journal 34(1):13-20.
2 Funck, J. W., Y. Zhong, D. A. Butler, C. C. Brunner, and J. B. Forrer. 2003. Image segmentation algorithms applied to wood defect detection. Computers and Electronics in Agriculture 41: 157-179.   DOI   ScienceOn
3 Huber, H. A., C. W. McMillin, and McKinney. 1985. Lumber defect detection abilities of furniture rough mill employees. Forest Prod. J. 35(11/12): 79-82.
4 Davies, E. R. 2005. Machine Vision : Theory, Algorithms, Practicalities. Morgan Kaufmann, Elsevier Third Edition, San Francisco, pp. 161-164, pp. 234-245.
5 Lewis, J. P. 1995, Fast Template Matching. Vision Interface, pp. 120-123.
6 Otsu, N. 1979. A thresholding selection method from grey-level histogram. IEEE Transactions on Systems and Man Cybernetics SMC-9: pp. 62-66.   DOI   ScienceOn
7 Shi, J. and C. Tomasi. 1994. Good Features to Track. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94), Seattle, pp. 593-600.   DOI
8 Zhang, Z. 1998. A Flexible New Technique for Camera Calibration. Technical Report, Microsoft Research, Redmond, WA 98052, USA:21p.
9 Kapur, J. N., P. K. Sahoo, and A. K. Wang. 1985. A new method for grey-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics and Image Processing 29: 273-285.   DOI   ScienceOn
10 김병남, 이형우. 2001. 화상처리에 의한 목재표면결함 식별에 관한 연구. 목재공학 29(2): 91-99.
11 김병남. 2006. 제재 및 재단공정 최적화를 위한 목재의 형상인식기술 개발. 전남대학교 박사학위논문.