scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Singular value decomposition method for the detection of defects in woven fabric refined by morphological operation

TL;DR: For the removal of the noise from the binary fabric image the morphological opening operation with the suitable structuring element is performed and 94.08% success rate of detection of defects is achieved.
Abstract: In this paper a new approach for the detection of defects in woven fabric is presented where the singular value decomposition (SVD) method is used. SVD basically removes the interlaced grating structure of the waft and warp of the fabric leaving aside the defective part of the fabric. An intensity threshold value along with the module of definite size is considered for the binarization of the background free fabric image. Finally, for the removal of the noise from the binary fabric image the morphological opening operation with the suitable structuring element is performed. The technique is tested on 287 fabric samples consisting of five different types of defects in three types of woven fabrics from TILDA database. 94.08% success rate of detection of defects is achieved.
Citations
More filters
Journal ArticleDOI
TL;DR: A novel and robust fabric defect detection method based on the low-rank representation (LRR) technique, implemented by dividing a image into some corresponding blocked matrices to reduce dimensions and applying eigen-value decomposition on blocked matrix instead of singular value decomposition (SVD) on original fabric image, which improves the accuracy and efficiency.
Abstract: In this paper, we propose a novel and robust fabric defect detection method based on the low-rank representation (LRR) technique. Due to the repeated texture structure we model a defects-free fabric image as a low-rank structure. In addition, because defects, if exist, change only the texture of fabric locally, we model them with a sparse structure. Based on the above idea, we represent a fabric image into the sum of a low-rank matrix which expresses fabric texture and a sparse matrix which expresses defects. Then, the LRR method is applied to obtain the corresponding decomposition. Especially, in order to make better use of low-rank structure characteristics we propose LRREB (low-rank representation based on eigenvalue decomposition and blocked matrix) method to improve LRR. LRREB is implemented by dividing a image into some corresponding blocked matrices to reduce dimensions and applying eigen-value decomposition (EVD) on blocked matrix instead of singular value decomposition (SVD) on original fabric image, which improves the accuracy and efficiency. No training samples are required in our methods. Experimental results show that the proposed fabric defect detection method is feasible, effective, and simple to be employed.

35 citations

Journal ArticleDOI
TL;DR: Matrix singular value decomposition technique is employed for the detection of defects in fabrics by reducing the computational duty of operating over the whole image and removing the interlaced warp–weft grating structure from ROI.
Abstract: Matrix singular value decomposition technique is employed for the detection of defects in fabrics. Firstly, a region of interest (ROI) containing the defect is identified by a proposed adaptive partitioning technique – thus reducing the computational duty of operating over the whole image. The ROI portion of fabric image is then divided into small nonoverlapping subimages to further reduce the computational complexity and the average singular values of the subimages are calculated. To remove the interlaced warp–weft grating structure from ROI, which is the global information in the fabric image, selected singular values associated with positive average singular numbers are rejected and the fabric image is reconstructed to yield the image of the defect. Since the resulting image is saturated with noise and some unconnected parts mainly due to dissimilarity of the subimage of the fabric structure, postprocessing is carried out by binarization and edge detection to yield the edge map of the defect. Validity ...

10 citations


Cites background from "Singular value decomposition method..."

  • ...Chen and Feng (2010), Mak and Tian (2010), and Chandra, Banerjee, and Datta (2011) have shown that distributions of data are changed due to the fabric defects....

    [...]

References
More filters
Book
11 Feb 1984
TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Abstract: Image Processing and Mathematical Morphology-Frank Y. Shih 2009-03-23 In the development of digital multimedia, the importance and impact of image processing and mathematical morphology are well documented in areas ranging from automated vision detection and inspection to object recognition, image analysis and pattern recognition. Those working in these ever-evolving fields require a solid grasp of basic fundamentals, theory, and related applications—and few books can provide the unique tools for learning contained in this text. Image Processing and Mathematical Morphology: Fundamentals and Applications is a comprehensive, wide-ranging overview of morphological mechanisms and techniques and their relation to image processing. More than merely a tutorial on vital technical information, the book places this knowledge into a theoretical framework. This helps readers analyze key principles and architectures and then use the author’s novel ideas on implementation of advanced algorithms to formulate a practical and detailed plan to develop and foster their own ideas. The book: Presents the history and state-of-the-art techniques related to image morphological processing, with numerous practical examples Gives readers a clear tutorial on complex technology and other tools that rely on their intuition for a clear understanding of the subject Includes an updated bibliography and useful graphs and illustrations Examines several new algorithms in great detail so that readers can adapt them to derive their own solution approaches This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.

9,566 citations


"Singular value decomposition method..." refers background in this paper

  • ...…× unitary matrix known as the left singular vectors of oiM , Σ is an )( nm × diagonal matrix with non negative real numbers on the diagonal and padded with suitable number of zeros (known as the singular values of oiM ) and V is a )( nn × unitary matrix, known as the right singular vectors of oiM ....

    [...]

G. Matheron1
01 Jan 1975

3,471 citations

Journal ArticleDOI
TL;DR: This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references, and suggests that the combination of statistical, spectral and model-based approaches can give better results than any single approach.
Abstract: The investment in an automated fabric defect detection system is more than economical when reduction in labor cost and associated benefits are considered. The development of a fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to the large number of fabric defect classes, which are characterized by their vagueness and ambiguity. Numerous techniques have been developed to detect fabric defects and the purpose of this paper is to categorize and/or describe these algorithms. This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features. The characterization of real fabric surfaces using their structure and primitive set has not yet been successful. Therefore, on the basis of the nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state-of-the-art, the limitations of several promising techniques are identified and performances are analyzed in the context of their demonstrated results and intended application. The conclusions from this paper also suggest that the combination of statistical, spectral and model-based approaches can give better results than any single approach, and is suggested for further research.

628 citations


"Singular value decomposition method..." refers background in this paper

  • ...The detection and classification of fabric defects means the identification and grouping of the defective fabric samples depending upon some similarity criteria....

    [...]

Book
01 Jan 1988

550 citations


"Singular value decomposition method..." refers background in this paper

  • ...…normalized singular value iNΣ is obtained as follows S mei i N −Σ=Σ (2) where, iNΣ is the i-th normalized singular value of the fabric image, iΣ is the i-th singular value of the fabric image, me is the mean of all the singular values of the fabric image and S is the standard deviation of all…...

    [...]

Book
25 Sep 1992
TL;DR: This book is designed to be of interest to optical, electrical and electronics, and electro-optic engineers, including image processing, signal processing, machine vision, and computer vision engineers, applied mathematicians, image analysts and scientists and graduate-level students in image processing and mathematical morphology courses.
Abstract: Presents the statistical analysis of morphological filters and their automatic optical design, the development of morphological features for image signatures, and the design of efficient morphological algorithms. Extends the morphological paradigm to include other branches of science and mathematics.;This book is designed to be of interest to optical, electrical and electronics, and electro-optic engineers, including image processing, signal processing, machine vision, and computer vision engineers, applied mathematicians, image analysts and scientists and graduate-level students in image processing and mathematical morphology courses.

435 citations