Defect Detection in Pattern Texture Analysis Based on Kernel Selection in Support Vector Machine

I  Manimozhi   and S  Janakiraman

doi:10.17485/ijst/2016/v9i45/106501

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Defect Detection in Pattern Texture Analysis Based on Kernel Selection in Support Vector Machine

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2016/v9i45/106501

Year: 2016, Volume: 9, Issue: 45, Pages: 1-4

Original Article

Defect Detection in Pattern Texture Analysis Based on Kernel Selection in Support Vector Machine

I. Manimozhi^1* and S. Janakiraman²

¹MVJ College of Engineering and Research Scholar in Manonmaniam Sundaranar University, Tirunelveli – 627012, TamilNadu, India; [email protected] ²Department of Banking Technology, School of Management, Pondicherry University, Pondicherry – 605014, TamilNadu, India; [email protected]

*Author for correspondence
I. Manimozhi MVJ College of Engineering and Research Scholar in Manonmaniam Sundaranar University, Tirunelveli – 627012, TamilNadu, India; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objective: Finding defects in real world application is assorted process. A robust and novel method is designed to select fine distinctions of features and classifying the images lead to improve the quality of products in industrial engineering. Methods/Statistical Analysis: Image feature accentuate, feature selection and classification are the different stages in pattern texture analysis. The efficiency of the overall system depends on efficiency of individual stages. Findings: Computational complexity of kernel algorithms are more intelligent than features .We analyzed and reviewed linear kernel, Quadratic Kernel, Polynomial Kernel, Sigmoid Kernel of SVM to classify the patterns effectively for classifying the defects. Improvements/Applications: Here kernel functions such as the polynomial kernel functions are yield superb performance ratios.

Keywords: Defect Detections, Feature Extractions, GTDM, Polynomial Kernel Functions, SVM