Automated surface inspection for directional textures

Image and Vision Computing

Volumn 18, Issue 1, 1999, Pages 49-62

  Tsai, D M ^a   Hsieh, C Y ^a  

^a YUAN ZE UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; FOURIER TRANSFORMS; HOUGH TRANSFORMS; STATISTICAL METHODS;

DEFECT DETECTION; DIRECTIONAL TEXTURES; SURFACE INSPECTION;

IMAGE RECONSTRUCTION;

EID: 0033331743     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0262-8856(99)00009-8     Document Type: Article

References (32)

1. Pikaz A., Averbuch A. An efficient topological characterization of gray-levels textures using a multiresolution representation. Graphical Models and Image Processing. 59:1997;1-17.
2. Newan T.S., Jain A.K. survey of automated visual inspection. Computer Vision and Image Understanding. 61:1995;231-262.
3. Wilder J. Finding and evaluating defects in glass. Freeman H. Machine Vision for Inspection and Measurement. 1989;237 Academic Press, New York.
4. J. Olsson, S. Gruber, Web process inspection using neural classification of scattering light, Proceedings of the IEEE International Conference on Industrial Electronics, Control, Instrumentation and Automation (IECON'92), 1992, pp. 1443-1448.
5. C. Fernandez, C. Platero, P. Campoy, R. Aracil, Vision system for on-line surface inspection in aluminum casting process, Proceedings of the IEEE International Conference on Industrial Electronics, Control, Instrumentation and Automation (IECON' 93), 1993, pp. 1854-1859.
6. Brzakovic D., Vujovic N. Designing defect classification system: a case study. Pattern Recognition. 29:1996;1401-1419.
7. A. Noble, V.D. Nguyen, C. Marinos, A.T. Tran, J. Farley, K. Hedengren, J.L. Mundy, Template guided visual inspection, Proceedings of the Second European Conference on Compute Vision, Santa Margherita Ligure, Italy, 1992, pp. 893-901.
8. Moganti M., Ercal F. Automatic PCB inspection Algorithms: a survey. Computer Vision and Image Understanding. 63:1996;287-313.
9. Gonzalez R.C., Woods R.E. Digital Image Processing. 1992;Addison-Wesley, Reading, MA.
10. Cohen F.S. Maximum likelihood unsupervised textured image segmentation. CVGIP: Graphical Models and Image Processing. 54:1992;239-251.
11. Van Hulle M.M., Tollenaere T. A modular artificial neural network for texture processing. Neural Networks. 6:1993;7-32.
12. Ramana K.V., Ramamoorthy B. Statistical methods to compare the texture features of machined surfaces. Pattern Recognition. 29:1996;1447-1459.
13. Haralick R.M., Shanmugam K., Dinstein I. Textural features for image classification. IEEE Trans. System, Man Cybernet. 3:1973;610-621.
14. T. Ojala, M. Pietikäinen, O. Silven, Edge-based texture measures for surface inspection, Proceedings of the 11th International Conference on Pattern Recognition, The Hague, The Netherlands, 1992, pp. B594-B598.
15. Conners R.W., McMillin C.W., Lin K., Vasquez-Espinosa R.E. Identifying and locating surface detects in wood. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5:1983;573-583.
16. Siew L.H., Hogdson R.M. Texture measures for carpet wear assessment. IEEE Trans. Pattern Anal. Mach. Intell. 10:1988;92-105.
17. Wechsler H. Texture analysis: a survey. Signal Processing. 2:1980;271-282.
18. Arof H., Deravi F. Circular neighborhood and 1D DFT features for texture classification and segmentation. IIE Proc. Vision, Image and Signal Processing. 145:1998;167-172.
19. Azencott R., Wang J.-P., Younes L. Texture classification using windowed Fourier filters. IEEE Trans. Pattern Anal. Mach. Intell. 19:1997;148-153.
20. Mashford J.S. A neural network image classification system for automatic inspection. Proc. IEEE Inter. Conf. Neural Networks. 2:1995;713-717.
21. Augusteijn M.F., Clemens L.E. A performance evaluation of texture measures for image classification and segmentation using cascade-correlation architecture. IEEE Inter. Conf. Neural Networks IEEE World Congress Comput. Intell. 7:1994;4300-4305.
22. Tsai D.-M., Chen J.-J., Chen J.-F. A vision system for surface roughness assessment using neural networks. Inter. J. Adv. Manuf. Technol. 14:1998;412-422.
23. Liu S.-S, Jernigan M.E. Texture analysis and discrimination in additive noise. Computer Vision, Graphics, Image Process. 49:1990;52-67.
24. Muzzolini R., Yang Y.-H., Pierson R. Texture characterization using robust statistics. Pattern Recognition. 27:1994;119-134.
25. T. Ohshige, H. Tanaka, Y. Miyazaki, T. Kanda, H. Ichimura, N. Kosaka, T. Tomoda, Defect inspection system for patterned wafers based on the spatial-frequency filtering, The 11th IEEE/CHMT international Electronics Manufacturing Technology Symposium, New York, 1991, pp. 192-196.
26. Porat M., Zeevi Y.Y. The generalized Gabor scheme of image representation in biological and machine vision. IEEE Trans. Pattern Anal. Mach. Intell. 10:1988;452-468.
27. Weldon T.P., Higgins W.E., Dunn D.F. Gabor filter design for multiple texture segmentation. Opt. Engng. 35:1996;2852-2863.
28. Jain A.K., Ratha N.K., Lakshmanan S. Object detection using Gabor filters. Pattern Recognition. 30:1997;295-309.
29. Soumekh M. Fourier Array Imaging. 1994;Prentice Hall, Englewood Cliffs, NJ.
30. P.V.C. Hough, A method and means for recognizing complex patterns, US Pattern 3069654, 1962.
31. Duda R.O., Hart P.E. Use of the Hough transformation to detect lines and curves in pictures. Commun. Assoc. Comput. Mach. 15:1972;11-15.
32. Davis E.R. Image space transforms for detecting straight edges in industrial images. Pattern Recognition Lett. 4:1986;185-192.