Defect detection in coloured texture surfaces using Gabor filters

References

• Newman, T. S. and Jain, A. K. A survey of automated visual inspection. Comput. Vision Image Understand., 1995, 61, 231–262.
   Web of Science ®Google Scholar
• Wilder, J. Finding and evaluating defects in glass. In Machine Vision for Inspection and Measurement (Ed. H. Freeman), 1989, pp. 237–255 (Academic Press, New York).
   Google Scholar
• Olsson, J. and Gruber, S. Web process inspection using neural classification of scattering light. In Proc. IEEE Int. Conference on Industrial Electronics Control: IECON’92, San Diego, 1992, pp. 1443–1448 (IEEE, Piscataway, NJ).
   Google Scholar
• Brzakovic, D. and Vujovic, N. Designing defect classification system: a case study. Pattern Recognition, 1999, 29, 1401–1419.
   Google Scholar
• Sum, C. and Wee, W. G. Neighboring gray level dependence matrix. Comput. Vision, Graphics, Image Process., 1982, 23, 341–352.
   Google Scholar
• Siew, L. H. and Hogdson, R. M. Texture measures for carpet wear assessment. IEEE Trans. Pattern Anal. Mach. Intel., 1988, 10, 92–105.
   Web of Science ®Google Scholar
• Liu, S.-S. and Jernigan, M. E. Texture analysis and discrimination in additive noise. Computer Vision, Graphics, Image Process., 1990, 49, 52–67.
   Google Scholar
• Daugman, J. G. Uncertainty relation for resolution in space, spatial-frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am., 1985, 2, 1160–1169.
   Web of Science ®Google Scholar
• Teuner, A., Pichler, O. and Hosticka, B. J. Unsupervised texture segmentation of images using tuned matched Gabor filters. IEEE Trans. Image Process., 1995, 4, 863–870.
   PubMed Web of Science ®Google Scholar
• Randen, T. and Husoy, J. H. Filtering for texture classification: a comparative study. IEEETrans. Pattern Anal. Mach. Intell., 1999, 21(4), 291–310.
   Web of Science ®Google Scholar
• Weldon, T. P., Higgins, W. E. and Dunn, D. F. Gabor filter design for multiple texture segmentation. Opt. Eng., 1996, 35, 2852–2863.
   Web of Science ®Google Scholar
• Reed, T. R. and Wechsler, H. Segmentation of textured images and Gestalt organization using spatial/spatial- frequency representations. IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, 1–12.
   Web of Science ®Google Scholar
• Namuduri, K. R., Mehrotra, R. and Ranganathan, N. Efficient computation of Gabor filter based multiresolution responses. Pattern Recognition, 1994, 27, 925–938.
   Web of Science ®Google Scholar
• Rahgu, P. P. and Yegnanarayana, B. Segmentation of Gabor-filtered textures using deterministic relaxation. IEEE Trans. Image Process., 1996, 5, 1625–1636.
   PubMed Web of Science ®Google Scholar
• Bovik, A. C., Clark, M. and Geisler, W. S. Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, 55–73.
   Web of Science ®Google Scholar
• Dunn, D. and Higgins, W. E. Optimal Gabor filters for texture segmentation. IEEE Trans. Image Process., 1995, 4, 947–964.
   PubMed Web of Science ®Google Scholar
• Weldon, T. P., Higgins, W. E. and Dunn, D. F. Efficient Gabor filter design for texture segmentation. Pattern Recognition, 1996, 29, 2005–2015.
   Web of Science ®Google Scholar
• Clausi, D. A. and Jernigan, M. E. Designing Gabor filters for optimal texture separability. Pattern Recognition, 2000, 33, 1835–1849.
   Web of Science ®Google Scholar
• Zhou, L., Chalana, V. and Kim, Y. PC-based machine vision system for real-time computer-aided potato inspection. Int. J. Imag. Syst. Technol., 1998, 9(6), 423–433.
   Web of Science ®Google Scholar
• Recce, M., Plebe, A., Taylor, J. and Tropiano, G. Video grading of oranges in real time. Artific. Intell. Rev., 1998, 12, 117–136.
   Web of Science ®Google Scholar
• Boukouvalas, C., Kittler, J., Marik, R. and Petrou, M. Color grading of randomly textured ceramic tiles using color histograms. IEEE Trans. Ind. Electr., 1999, 46(1), 219–226.
   Web of Science ®Google Scholar
• Song, K. Y., Kittler, J. and Petrou, M. Defect detection in random colour textures. Image Vision Comput., 1996, 14, 667–683.
   Web of Science ®Google Scholar
• Tafuri, M., Branca, A., Attolico, G., Distante, A. and Delancy, W. Automatic leather inspection of defective patterns. Proc. SPIE, 1996, pp. 108–119 (SPIE, Bellingham, WA).
   Google Scholar
• Han, Y. J., Cho, Y.-J., Lambert, W. E. and Bragg, C. K. Identification and measurement of convolutions in cotton fiber using image analysis. Artif. Intell. Rev., 1998, 12, 201–211.
   Web of Science ®Google Scholar
• Barth, M., Hirayama, D., Beni, G. and Hackwood, S. A color vision inspection system for integrated circuit manufacturing. IEEE Trans. Semicond. Manuf., 1992, 5 (4), 290–301.
   Web of Science ®Google Scholar
• Nakashima, K. Hybrid inspection system for LCD color filter panels. In IEEE Instrumentation and Measurement Technology Conf., 1994, pp. 689–692 (IEEE, Piscataway, NJ).
   Google Scholar
• Novak, C. L. and Shafer, S. A. Methods for estimating scene parameters from color histograms. J. Opt. Soc. Am. A, 1994, 11(11), 3020–3036.
   Google Scholar
• Healey, G. and Slater, D. Global color constancy: recognition of objects by use of illumination invariant properties of color distribution. J. Opt. Soc. Am. A, 1994, 11(11), 3003–3010.
   Google Scholar
• Gevers, T. and Smeulders, A. W. M. Color-based object recognition. Pattern Recognition, 1999, 32, 453–464.
   Web of Science ®Google Scholar
• Suen, P.-H. and Healey, G. Modeling and classifying color textures using random fields in a random environment. Pattern Recognition, 1999, 32, 1009–1017.
   Web of Science ®Google Scholar
• Van de Wouwer, G., Scheunders, P., Livens, S. and van Dyck, D. Wavelet correlation signatures for color texture characterization, Pattern Recognition, 1999, 32, 443–451.
   Web of Science ®Google Scholar
• Zugaj, D. and Lattuati, V. A new approach for color images segmentation based on fusing region and edge segmentations outputs. Pattern Recognition, 1998, 31, 105–113.
   Web of Science ®Google Scholar
• Campadelli, P., Medici, D. and Schettini, R. Color image segmentation using Hopfield networks. Image Vision Comput., 1997, 15, 161–166.
   Web of Science ®Google Scholar
• Verikas, A., Malmqvist, K. and Bergman, L. Colour image segmentation by modular neural network. Pattern Recognition Lett., 1997, 18, 173–185.
   Web of Science ®Google Scholar
• Liu, J. and Yang, Y. H. Multiresolution color image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 1994, 16, 689–700.
   Web of Science ®Google Scholar
• Panjwani, D. K. and Healey, G. Markov random fields for unsupervised segmentation of texture color images. IEEE Trans. Pattern Anal. Mach. Intell., 1995, 17, 939–954.
   Web of Science ®Google Scholar
• Fairchild, M. D. Color Appearance Models, 1998 (Addison Wesley, Reading, MA).
   Google Scholar
• Gabor, D. Theory of communication. J. Inst. Electr. Eng., 1946, 93, 429–457.
   Google Scholar
• Tsai, D. M., Wu, S.-K. and Chen, M.-C. Optimal Gabor filter design for texture segmentation using stochastic optimization. Image Vision Comput., 2001, 19, 299–316.
   Web of Science ®Google Scholar
• Tsai, D. M. and Hsieh, C.-Y. Automated surface inspection for directional textures. Image Vision Comput., 1999, 18, 49–62.
   Web of Science ®Google Scholar