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Journal of Sensors
Volume 2016 (2016), Article ID 9794723, 8 pages
Research Article

Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

Central University of Technology, Free State, 20 President Brand Street, Bloemfontein 9301, South Africa

Received 25 July 2016; Accepted 10 October 2016

Academic Editor: Calogero M. Oddo

Copyright © 2016 Hermanus Vermaak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The dual-tree complex wavelet transform (DTCWT) solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT). It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG), are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT) with a detection rate of 4.5% to 15.8% higher depending on the fabric type.