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Information Technology Journal
  Year: 2011 | Volume: 10 | Issue: 9 | Page No.: 1701-1708
DOI: 10.3923/itj.2011.1701.1708
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Fabric Defect Detection using Undecimated Wavelet Transform

Bi Mingde and Sun Zhigang

In this study, a new fabric defect detection algorithm base on undecimated wavelet transform is proposed. The selection scheme of wavelet decomposition scales is investigated to set the decomposition scales adaptively to the fabric texture. The objective of the scheme is to enhance the energy of defective region and attenuate the energy of non-defective region. And the performance of detection results with different number of decomposition scales is also discussed. A simple and computationally effective data fusion scheme combined with amplitudes division of wavelet coefficients is used to fuse data from multiple scales together. And several features based on defective energy estimation are extracted from fused image. By examining the extracted features the proposed algorithm can provide not only the location of defects but also some detailed information about them which can be used for defect recognition and classification. Experimental results of real fabric defects are provided to validate the effectiveness and robustness of the defect detection algorithm.
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  •    Textural Fabric Defect Detection using Adaptive Quantized Gray-level Co-occurrence Matrix and Support Vector Description Data
  •    Texture Characterisation and Classification Using Full Wavelet Decomposition
  •    Introduction to Multitone Multiresolution Wavelet Analysis
  •    Texture Classification Based on Extraction of Skeleton Primitives Using Wavelets
  •    K- Means Clustering Based Image Compression in Wavelet Domain
  •    Research on the Image Segmentation Necessity for Regions-Based Image Processing
How to cite this article:

Bi Mingde and Sun Zhigang, 2011. Fabric Defect Detection using Undecimated Wavelet Transform. Information Technology Journal, 10: 1701-1708.

DOI: 10.3923/itj.2011.1701.1708






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