Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection
Joint Authors
Vermaak, Hermanus
Nsengiyumva, Philibert
Luwes, Nicolaas
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-07
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Vermaak, Hermanus& Nsengiyumva, Philibert& Luwes, Nicolaas. 2016. Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection. Journal of Sensors،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1110725
Modern Language Association (MLA)
Vermaak, Hermanus…[et al.]. Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection. Journal of Sensors No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1110725
American Medical Association (AMA)
Vermaak, Hermanus& Nsengiyumva, Philibert& Luwes, Nicolaas. Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1110725
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110725