Wood defect detection using grayscale images and an optimized feature set

IECON Proceedings (Industrial Electronics Conference)

Volumn , Issue , 2006, Pages 3408-3412

  Cavalin, P ^b   Oliveira, L S ^a,b   Koerich, A L ^a,b   Britto Jr , A S ^a,b  

^a PONTIFICAL CATHOLIC UNIVERSITY OF PARANÁ   (Brazil)

^b INVISYS Intelligent Vision Systems   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COLOR IMAGE PROCESSING; ELECTRONICS INDUSTRY; EXPERIMENTS; EXTRACTION; FEATURE EXTRACTION; GENETIC ALGORITHMS; IMAGE ENHANCEMENT; INDUSTRIAL ELECTRONICS; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

ANNUAL CONFERENCE; CO-OCCURRENCE MATRICES; COLOR IMAGING; COMPARATIVE EXPERIMENTS; COMPETITIVE PERFORMANCE; DEFECT DETECTION; FEATURE SELECTION; FEATURE SELECTION ALGORITHMS; FEATURE SETS; GRAY-SCALE IMAGES; LEARNING PARADIGMS; MULTI-OBJECTIVE GENETIC ALGORITHMS;

WOOD;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELECTRONICS; EXTRACTION; NEURAL NETWORKS; WOOD;

EID: 50249085309     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IECON.2006.347618     Document Type: Conference Paper

References (12)

1. E. H. Aria. M. R. Saradjian, J. Amini, and C. Lucas. Generalized cooccurrence matrix to classify irs-1d images using neural network. In Proceedings of XXth ISPRS Congress, Instanbul, Turkey, July 2004.
2. C. C. Brunner, A. G. Maristany, D. A. Butler. D. Vanleeuwen, and J. W. Funck. An evaluation of color spaces for detecting defects in Douglas-fir veener. Industrial Metrology, pages 169-184, 1992.
3. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/ ~cjlin/libsvm.
4. nd edition, April 2002.
5. H. Kauppinen. Development of A Color Machine Vision Method for Wood Surface Inspection. PhD thesis, Oulu University Library, 1999.
6. H. Kauppinen. A two stage defect recognition method for parquet slab grading. In Procs of the International Conference on Pattern Recognition, pages 803-806, 2000.
7. H. Kauppinen and O. Silven. The effect of illumination variations on color-based wood defect classification. In Procs of the International Conference on Pattern Recognition, pages 828-832, 1996.
8. L. S. Oliveira, R. Sabourin, F. Bortolozzi, and C. Y. Suen. A methodology for feature selection using multi-objective genetic algorithms for handwritten digit string recognition. International Journal of Pattern Recognition and Artificial Intelligence. 17(6):903-930, 2003.
9. S. Radovan, P. George, M. Panagiotis, G. Manos, A. Robert, and D. Igor, An approach for automated inspection of wood boards. In Procs of the International Conference on Image Processing, pages 798-801, 2001.
10. P. J. Sobey and E. C. Semple. Detection and sizing visual features in wood using tonal measures and a classification algorithm. Pattern Recogntion, 22:367-380, 1989.
11. G. V. Trunk. A problem of dimensionality: A simple example. IEEE Trans.on Pattern Analysis and Machine Intelligence. 1(3):306-307. 1979.
12. V. Vapnik. Statistical Learning Theory. John Wiley and Sons. 1998.