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Author: Ajay Kumar Pathak
Publisher: Open Dissertation Press
ISBN: 9781374782891
Category :
Languages : en
Pages :
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This dissertation, "Automated Defect Detection in Textured Materials" by Ajay Kumar, Pathak, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b3124222 Subjects: Materials - Texture - Inspection Materials - Defects Engineering inspection - Mathematical models
Author: Ajay Kumar Pathak
Publisher: Open Dissertation Press
ISBN: 9781374782891
Category :
Languages : en
Pages :
Get Book Here
Book Description
This dissertation, "Automated Defect Detection in Textured Materials" by Ajay Kumar, Pathak, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b3124222 Subjects: Materials - Texture - Inspection Materials - Defects Engineering inspection - Mathematical models
Author: Ajay Kumar Pathak
Publisher:
ISBN:
Category : Engineering inspection
Languages : en
Pages :
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Author: Yuk-Tung Henry Ngan
Publisher:
ISBN: 9781361469804
Category :
Languages : en
Pages :
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This dissertation, "Motif-based Method for Patterned Texture Defect Detection" by Yuk-tung, Henry, Ngan, 顏旭東, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b4020360 Subjects: Image processing - Mathematical models Computer vision Wallpaper - Testing
Author: Pai Peng
Publisher: Open Dissertation Press
ISBN: 9781361476772
Category :
Languages : en
Pages :
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This dissertation, "Automated Defect Detection for Textile Fabrics Using Gabor Wavelet Networks" by Pai, Peng, 彭湃, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Automated Defect Detection for Textile Fabrics Using Gabor Wavelet Networks submitted by PENG PAI for the degree of Doctor of Philosophy at The University of Hong Kong in December 2006 This study seeks to develop efficient methodologies to facilitate automated detection of defects in textile fabrics. Its novelty consists in combining the practical implementation of feature extraction and learning techniques by using Gabor wavelet networks (GWNs) for object representation. The study develops three structure design algorithms to determine automatically the number of hidden nodes in a GWN. The first algorithm is based on a pyramid decomposition approach, and can be used to design wavelet networks. The second algorithm is based on two important properties of GWNs, and is developed specifically for designing GWNs to solve fabric defect detection problems. These properties, which are formally established in this study, indicate that: (1) the magnitude of the network weight associated with a wavelet of a GWN trained by using an objective function governs the contribution of the wavelet in reconstructing the function; and (2) in the network training process, the translation parameters of a wavelet in the network are likely to position at the edge region of the objective function being studied. The third algorithm is based on the concept of orthogonal forward selection, and can be used to design wavelet networks for solving small and medium sized problems. For larger problems, the algorithm can be used to supplement other structure design algorithms to reduce the size of the network. A new defect detection scheme which employs 2D GWNs is proposed in this study. A superwavelet is used to ensure correct alignment between a template image and the corresponding sample images. However, the complexity analysis of the proposed scheme indicates that it is computationally demanding. To overcome this limitation, a 1D version of the above scheme which does not employ a superwavelet is developed to speed up the detection process. The scheme's good defect detection performance is confirmed by using offline experiments and by using real time experiments conducted with the prototyped automated inspection system developed in this study. The deployment of a GWN to extract features from a non-defective fabric image for the purpose of designing "optimal" Gabor filters and "optimal" morphological filters is investigated. These "optimal" filters are then used to design three defect detection schemes for textile fabrics. Another filter design method based on a real Gabor wavelet network is also proposed. The method automatically tunes the real parts of the Gabor functions to match the texture being studied. Based on these tuned-matched Gabor wavelets, a new defect detection scheme for textile fabrics is developed. The performances of all schemes are evaluated offline and in real time by using a variety of homogeneous textile fabric images. The study also proposes a complex-valued wavelet network (CVWN), which employs complex-valued multi-dimensional Gabor wavelets as the transfer functions. The feasibility and effectiveness of the CVWN are shown by solving a complicated feature extraction problem. Indeed, it can be noticed that a CVWN can be separated into two real-valued wavelet networks, namely a Gabor wavelet network and a real Gabor wavelet network. DOI: 10.5353/th_b387661
Author: Hong-Dar Lin
Publisher:
ISBN:
Category :
Languages : en
Pages : 6
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Author: Zhen Hou
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ISBN:
Category :
Languages : en
Pages : 248
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Author: Frank Boukamp
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Category :
Languages : en
Pages :
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Author: Jenelle Armstrong Piepmeier
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Category : Poultry
Languages : en
Pages : 130
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Author: Zhen Hou
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Category :
Languages : en
Pages : 11
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Author:
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ISBN:
Category :
Languages : en
Pages :
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The purpose of this research project is to show that a computerized system based on image processing software is capable of identifying defects in woven fabrics. Current defect detection is carried out through use of visual inspection of fabric rolls after the rolls have been doffed from the production machinery, which adds a substantial lag between defect creation and detection. Existing methods for automatic defect detection rely on methods that suffer from substantial analysis time or a low percentage of detection. The method described in this thesis represents a quick and accurate approach to automatic defect detection and is capable of identifying defects such as lines, tears, and spots. Utilizing a Genetic Algorithm (GA) as the primary means of solving the wavelet filter equations with respect to a fabric image proved adequate in the construction of a wavelet filter that was capable of removing large amounts of the fabric texture from the image, thus allowing defect segmentation algorithms to run more effectively. Although a real-time system is not developed, suggestions for constructing such a system are presented. This work provides a foundation for the development of a real-time automated defect detector based on the algorithms and methodologies employed in this work.
