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

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dc.contributor.author Vermaak, Hermanus
dc.contributor.author Nsengiyumva, Philibert
dc.contributor.author Luwes, Nicolaas
dc.date.accessioned 2017-11-21T07:38:05Z
dc.date.available 2017-11-21T07:38:05Z
dc.date.issued 2016
dc.identifier.issn 1687-7268
dc.identifier.uri http://hdl.handle.net/11462/1269
dc.description Published Article en_US
dc.description.abstract 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. en_US
dc.format.extent 297 699 bytes, 1 file
dc.format.mimetype Application/PDF
dc.language.iso en_US en_US
dc.publisher Journal of Sensors en_US
dc.title Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection en_US
dc.type Article en_US

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