Abstract
This paper describes various techniques to detect defects in textile images. These techniques are based on multichannel Gabor features. The building blocks of our approaches are: a modified principal component analysis (PCA) technique, to select the most relevant features; one-class classification techniques (a global Gaussian model, a nearest neighbor method, and a local Gaussian model). Experimental results on synthetic and real fabric images testify for the good performance of the methods considered.
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Beirão, C.L., Figueiredo, M.A.T. (2004). Defect Detection in Textile Images Using Gabor Filters. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_102
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DOI: https://doi.org/10.1007/978-3-540-30126-4_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
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