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<p>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.</p>
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

F. Cohen, Z. Fan and S. Attali, "Automated Inspection of Textile Fabrics Using Textural Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 803-808, 1991.
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