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Defect detection in pattern texture analysis using improved support vector machine

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Abstract

The detection of abnormalities is a very challenging problem in computer vision. In our proposed method designed for detecting the defect of pattern texture analysis. The preprocessing input image features are extracted using the Gray level co-occurrence matrix (GLCM) and Gray level run-length matrix (GLRLM). Then the extracted features are fed to the input of classification stage. Here the classification is done by improved support vector machine (ISVM) based on kernel analysis. Based on the improved support vector machine the features are classified. Final stage is segmentation; here the classified features are segmented using the modified fuzzy c means algorithm (MFCM).

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Authors and Affiliations

  1. MVJ College of Engineering, Manonmaniam Sundaranar University, Tirunelveli, India

    I. Manimozhi

  2. Department of Banking Technology, School of Management, Pondicherry University, Pondicherry, India

    S. Janakiraman

Authors
  1. I. Manimozhi
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  2. S. Janakiraman
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Correspondence to I. Manimozhi or S. Janakiraman.

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Manimozhi, I., Janakiraman, S. Defect detection in pattern texture analysis using improved support vector machine. Cluster Comput 22 (Suppl 6), 15223–15230 (2019). https://doi.org/10.1007/s10586-018-2551-y

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  • DOI: https://doi.org/10.1007/s10586-018-2551-y

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