Genetic input selection to a neural classifier for defect classification of radiata pine boards

Forest Products Journal

Volumn 53, Issue 7-8, 2003, Pages 87-94

  Estévez, Pablo A ^a   Perez, Claudio A ^a   Goles, Eric ^a  

^a UNIVERSITY OF CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; COSTS; DATABASE SYSTEMS; GENETIC ALGORITHMS; IMAGING SYSTEMS; VIDEO CAMERAS;

PINE BOARDS;

WOOD PRODUCTS;

ALGORITHMS; CAMERAS; COSTS; DATA BASES; IMAGE ANALYSIS; WOOD PRODUCTS;

AVES;

EID: 0042925724     PISSN: 00157473     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. Astrand, E. and A. Aström. 2000. The smart image sensor tracheid scanner - from research to product. In: Proc. of the Fourth Inter. Conf. on Image Processing and Scanning of Wood. Mountain Lake, VA. USDA Forest Serv., Southern Res. Sta., Asheville, NC. pp. 105-114.
2. Battiti, R. 1994. Using mutual information for selecting features in supervised neural net learning, IEEE Trans. Neural Networks 5(4):537-550.
3. Cho, T.H., R.W. Conners, and P.A. Araman. 1991. A comparison of rule-based, k-nearest neighbour and neural net classifiers for automated industrial inspection. In: Proc. IEEE/ACM Inter. Conf. on Developing and Managing Expert System Programs. Washington, DC. pp. 202-209. http://ieeexplore.ieee.org/Xplore.
4. Conners, R.W, C.W. McMillan, and C.N. Ng. 1985. The utility of color information in location and identification of defects in surfaced hardwood lumber. In: Proc. of the First Inter. Conf. on Scanning Technology in Sawmilling. Miller-Freeman Publications, San Francisco, CA. pp. XVIII 1-33.
5. _, D.E. Kline, P.A. Araman, and T.H. Drayer. 1997. Machine vision technology for the forest products industry. IEEE Computer 30(7):43-48.
6. Conners, R.W., C.W. McMillin, K. Lin, and R.E. Vasquez-Espinosa. 1983. Identifying and locating surface defects in wood: Part of an automated lumber processing system. IEEE Trans. Pattern Anal. Machine Intell. 5(6):573-583.
7. Estévez, P. A. and R. Caballero. 1998. A niching genetic algorithm for selecting features for neural network classifiers. In: Perspectives in Neural Computation (ICANN-98). Vol. I. Springer-Verlag. pp. 311-316.
8. _, M. Fernández, R. Alcock, and M. Packianather. 1999, Selection of features for the classification of wood board defects. In: Inter. Conf. on Artificial Neural Networks (ICANN-99). Vol. I. Edinburgh, UK. The Institution of Electrical Engineers, London, pp. 347-352.
9. _, C. Perez, R. Caballero, G. Buhler, and E. Goles, 1998. Classification of defects on wood boards based on neural networks and genetic selection of features. In: HIS World Multiconference on Systemics, Cybernetic and Informatics (SCI'98). Vol 1. Inter. Inst. of Informatics and Systemics, Orlando, FL. pp. 624-629.
10. Funck, J.W., Y. Zhong, D.A. Butler, C.C. Brunner, and J.P. Forrer. 2000. A performance analysis of image segmentation algorithms as applied to wood surface feature detection. In: Proc. of the Fourth Inter. Conf. on Image Processing and Scanning of Wood. Mountain Lake, VA. pp. 129-141.
11. Haykin, S. 1994. Neural Networks, A Comprehensive Foundation. Macmillan, Englewood Cliffs, NJ. 696 pp.
12. Karsulovic, J.T., L.A. León, and M.I. Dinator. 1999. The use of linear attenuation coefficients of gamma radiation for detecting knots in Pinus radiata. Forest Prod. J. 49(2):73-76.
13. _, _, and L. Gaete. 2000. Ultrasonic detection of knots and annual ring orientation in Pinus radiata lumber. Wood and Fiber Sci. 32(3):278-286.
14. Kline, D.E., C. Surak, and P.A. Araman, 2000. Evaluation of a multi-sensor machine vision system for automated hardwood lumber grading, In: Proc. of the Fourth Inter Conf. on Image Processing and Scanning of Wood. Mountain Lake, VA. pp. 75-87.
15. _, A. Widoyoko, J.K. Wiedenbeck, and P. A. Araman. 1998. Performance of color camera machine vision in automated furniture rough mill systems. Forest Prod. J. 48(3):38-45.
16. Lampinen, J., S. Smolander, O. Silven, and H. Kauppinen. 1994. Wood defect recognition: A comparative study. In: Proc. of Workshop on Machine Vision in Advanced Production. Oulu, Finland. 1-3.6. www.Ice.hut.fi/̃jlampine/jl_bib.html.
17. McMillin, C.W., R.W. Conners, and H.A. Huber. 1984. ALPS: A potential new automated lumber processing system, Forest Prod. J. 34(1):13-20.
18. Mitchell, M. 1996. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA. 205 pp.
19. Nyström, J. 2000. Automatic measurement of fiber orientation in softwoods by using the tracheid-effect. In: Proc. of the Fourth Inter. Conf. on Image Processing and Scanning of Wood. Mountain Lake, VA. pp. 97-104.
20. Patton, M.D. and R.L. King. 2000. Seedlet technology for anomaly detection. In: Proc. of the Fourth Inter. Conf. on Image Processing and Scanning of Wood. Mountain Lake, VA. pp. 163-176.
21. Pham, D.T. and R.J. Alcock. 1996. Automatic detection of defects on birch wood boards. In: Proc. Institution of Mechanical Engineers Part E. J. of Process Mechanical Engineering 210:45-52.
22. _ and _. 1998. Automated grading and defect detection: A review. Forest Prod. J. 48(4):34-42.
23. _ and _. 1999a. Synergistic classification systems for wood defect identification. Proc. Institution of Mechanical Engineers Part E. J. of Process Mechanical Engineering 213(2):127-134.
24. _ and _. 1999b. Automated visual inspection of wood boards: Selection of features for defect classification. In: Proc. Institution of Mechanical Engineers Part E. J. of Process Mechanical Engineering 213(4):79-87.
25. Quin, F., P.H. Steele, and R. Shmulsky. 1998. Locating knots in wood with an infrared detector system. Forest Prod. J. 48(10):80-84.
26. Saito, K. and R. Nakano. 1997. Partial BFGS update and efficient step-length calculation for three-layer neural networks. Neural Computation 9:123-141.
27. Sharkey, A.J.C. (ed.). 1999. Combining Artificial Neural Nets. Springer-Verlag, London. 298 pp.
28. Steele, P.H., L. Kumar, and R. Shmulsky. 2000. Differentiation of knots, distorted grain, and clear wood by radio-frequency scanning. Forest Prod. J. 50(3):59-62.
29. Szymani, R. and K.A. McDonald. 1981. Defect detection in lumber: State of the art. Forest Prod. J. 31(11):34-44.