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Automated Inspection of Textile Fabrics Using Textural Models
August 1991 (vol. 13 no. 8)
pp. 803-808

The authors discuss the problem of textile fabric inspection using the visual textural properties of the fabric. The problem is to detect and locate the various kinds of defects that might be present in a given fabric sample based on an image of the fabric. Stochastic models are used to model the visual fabric texture. The authors use the Gaussian Markov random field to model the texture image of nondefective fabric. The inspection problem is cast as a statistical hypothesis testing problem on statistics derived from the model. The image of the fabric patch to be inspected is partitioned into nonoverlapping windows of size N*N where each window is classified as defective or nondefective based on a likelihood ratio test of size alpha . The test is recast in terms of the sufficient statistics associated with the model parameters. The sufficient statistics are easily computable for any sample. The authors generalize the test when the model parameters of the fabric are assumed to be unknown.

[1] R. Chin and C. Harlow, "Automated visual inspection: A survey,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-4, no. 6, Nov. 1982.
[2] R. Conners, "Towards a set of statistical features which measure visually perceivable qualities of textures," inProc. IEEE Conf. PRIP, 1979.
[3] R. Haralicket al., "Texture features for image classification,"IEEE Trans. Syst., Man, Cybern., vol. SMC-3, 1973.
[4] B. Julesz, "Visual pattern discrimination,"IRE Trans. Inform. Theory, vol. IT-8, pp. 84-92, 1962.
[5] R. Cross and A. Jain, "Markov random field texture models,"IEEE Pattern Anal. and Machine Intell., vol. PAMI-5, 1983.
[6] J. Woods, "Two-dimensional discrete Markov random fields,"IEEE Trans. Inform. Theory,vol. IT-18, pp. 232-240, 1972.
[7] J. Besag, "Spatial interaction and the statistical analysis of lattice systems,"J. Roy. Statist. Soc., series B, vol. 36, 1974.
[8] R. L. Kashyap, R. Chellappa, and A. Khotanzad, "Texture classification using features derived from random field models,"Pattern Recognition Lett., vol. 1, Oct. 1982.
[9] R. Kashyap and R. Chellapa, "Estimation and choice of neighbors in spatial interaction models of images,"IEEE Trans. Inform. Theory, vol. IT-29, pp. 60-72, Jan. 1983.
[10] J. Besag and P. Moran, "On the estimation and testing of spatial interaction in Gaussian lattices,"Biometrika, vol. 62, 1975.
[11] R. Chellappa and Chatterjee, "Texture classification using GMRF models,"IEEE Trans. Acoust., Speech, Signal Processing, Aug. 1985.
[12] C. Therrien, "An estimation-theoretic approach to terrain image segmentation,"Comput. Graphics Image Processing, vol. 22, 1983.
[13] S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, Nov. 1984.
[14] H. Derin and H. Elliott, "Modeling and segmentation of noisy and textured images using Gibbs random fields,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9, pp. 39-55, Jan. 1987.
[15] R. Cristi, "Multilevel image segmentation by Markov random field models and Kalman filtering," inProc. Int. Conf. Acoustics, Speech, and Signal Processing, NY, Apr. 1988.
[16] F. S. Cohen,Markov Random Fields for Image Modelling and Analysis, U. Desai, Ed. New York: Kluwer Academic, Sept. 1986.
[17] F. S. Cohen and D. B. Cooper, "Simple parallel hierarchical and relaxation algorithms for segmenting noncausal Markovian fields,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9, pp. 195-219, March 1987.
[18] Z. Fan and F.S. Cohen, "Textured image segmentation as a multiple-hypothesis test,"IEEE Trans. Syst. Circuits, June 1988.
[19] A. Rosenfeldet al., "Visual texture analysis," Univ. Maryland, Tech. Rep. 70-116, June 1970.
[20] S. Zuckeret al., "Picture segmentation by texture discrimination,"IEEE Trans. Comput., vol. C-24, Dec. 1975.
[21] S. Fuet al., "Stochastic tree grammar inference for texture synthesis and discrimination,"Comput. Vision, Graphics, Image Processing, vol. 9, 1979.
[22] F. Tomitaet al., "Description of texture by a structural analysis," inProc. IJCAI-79, 1979.
[23] R. Connerset al., "Identifying and locating surface defects in wood,"IEEE Trans. Pattern Anal., Machine Intell., vol. 5, no. 6, Nov. 1983.
[24] K. Pietrzak, "Automatic surface inspection based on visual texture analysis," M.Sc. thesis, Dep. Elec. Eng., Univ. Rhode Island, 1985.
[25] H. Giebelet al., "A system for automatic inspection of glass bottles using texture analysis procedures," inProc. IEEE Sixth Int. Conf. Pattern Recognition, vol. 2, 1982.
[26] H. Donet al., "Metal surface inspection using image processing techniques,"IEEE Trans. Syst., Man, Cybern., vol. SMC-14, Feb. 1984.
[27] A. Knoll, "Automatic fabric inspection," Textile Inst. Industry, Jan. 1985.
[28] S. Takamatsu, "Preparations and fabric inspection,"J. Textile Mach. Soc. Japan, vol. 29, 1976.
[29] E. Kuse, "On the automated systems of fabric inspection and sewing in the USA,"J. Soc. Fiber Sci. Technol. Japan, vol. 34, 1978.
[30] H. Koshimizu, "Fundamental study on automatic fabric inspection by computer image processing," inProc. SPIE Imaging Applications for Automatic Industrial Inspection and Assembly, vol. 182, 1979.
[31] S. Zacks,Parametric Statistical Inference. New York: Pergamon, 1981.
[32] E. L. Lehmann,Testing Statistical Hypotheses. New York: Wiley, 1959.
[33] D. B. Cooper, "When should a machine ask for help?"IEEE Trans. Inform. Theory, vol. 12, no. 4, July 1974.
[34] R. M. Bolle and D.B. Cooper, "On optimally combining pieces of information, with application to estimating 3-D complex-object position from range data,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, pp. 619-638, Sept. 1986.
[35] R. D. Rimey and F. S. Cohen, "A maximum-likelihood approach for segmenting range data,"IEEE Trans. Robotics Automat., vol. 4, no. 3, June 1988.

Index Terms:
textural models; textile fabric inspection; visual textural properties; Gaussian Markov random field; nondefective fabric; statistical hypothesis testing; likelihood ratio test; inspection; Markov processes; pattern recognition; statistical analysis; textile industry
Citation:
F.S. Cohen, Z. Fan, S. Attali, "Automated Inspection of Textile Fabrics Using Textural Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 803-808, Aug. 1991, doi:10.1109/34.85670
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