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Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition.

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Title: Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition.
Authors: Yan, Hao1 (AUTHOR) yanhao@gatech.edu, Paynabar, Kamran1 (AUTHOR), Shi, Jianjun1 (AUTHOR)
Source: Technometrics. Feb2017, Vol. 59 Issue 1, p102-114. 13p.
Subjects: Anomaly detection (Computer security), Computer debugging software, Regression analysis data processing, Image analysis, Wavelets (Mathematics), Equipment & supplies
Abstract: In various manufacturing applications such as steel, composites, and textile production, anomaly detection in noisy images is of special importance. Although there are several methods for image denoising and anomaly detection, most of these perform denoising and detection sequentially, which affects detection accuracy and efficiency. Additionally, the low computational speed of some of these methods is a limitation for real-time inspection. In this article, we develop a novel methodology for anomaly detection in noisy images with smooth backgrounds. The proposed method, named smooth-sparse decomposition, exploits regularized high-dimensional regression to decompose an image and separate anomalous regions by solving a large-scale optimization problem. To enable the proposed method for real-time implementation, a fast algorithm for solving the optimization model is proposed. Using simulations and a case study, we evaluate the performance of the proposed method and compare it with existing methods. Numerical results demonstrate the superiority of the proposed method in terms of the detection accuracy as well as computation time. This article has supplementary materials that includes all the technical details, proofs, MATLAB codes, and simulated images used in the article. [ABSTRACT FROM PUBLISHER]
Copyright of Technometrics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition.
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  Data: <searchLink fieldCode="AR" term="%22Yan%2C+Hao%22">Yan, Hao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yanhao@gatech.edu</i><br /><searchLink fieldCode="AR" term="%22Paynabar%2C+Kamran%22">Paynabar, Kamran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Jianjun%22">Shi, Jianjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Technometrics%22">Technometrics</searchLink>. Feb2017, Vol. 59 Issue 1, p102-114. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+debugging+software%22">Computer debugging software</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis+data+processing%22">Regression analysis data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelets+%28Mathematics%29%22">Wavelets (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Equipment+%26+supplies%22">Equipment & supplies</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In various manufacturing applications such as steel, composites, and textile production, anomaly detection in noisy images is of special importance. Although there are several methods for image denoising and anomaly detection, most of these perform denoising and detection sequentially, which affects detection accuracy and efficiency. Additionally, the low computational speed of some of these methods is a limitation for real-time inspection. In this article, we develop a novel methodology for anomaly detection in noisy images with smooth backgrounds. The proposed method, named smooth-sparse decomposition, exploits regularized high-dimensional regression to decompose an image and separate anomalous regions by solving a large-scale optimization problem. To enable the proposed method for real-time implementation, a fast algorithm for solving the optimization model is proposed. Using simulations and a case study, we evaluate the performance of the proposed method and compare it with existing methods. Numerical results demonstrate the superiority of the proposed method in terms of the detection accuracy as well as computation time. This article has supplementary materials that includes all the technical details, proofs, MATLAB codes, and simulated images used in the article. [ABSTRACT FROM PUBLISHER]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Technometrics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00401706.2015.1102764
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      – Code: eng
        Text: English
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      – SubjectFull: Computer debugging software
        Type: general
      – SubjectFull: Regression analysis data processing
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      – SubjectFull: Image analysis
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      – SubjectFull: Wavelets (Mathematics)
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      – SubjectFull: Equipment & supplies
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      – TitleFull: Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition.
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              M: 02
              Text: Feb2017
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