Title: An efficient approach for defect detection in pattern texture analysis using an improved support vector machine

Authors: I. Manimozhi; S. Janakiraman

Addresses: MVJ College of College of Engineering, Manonmaniam Sundaranar University, Tirunelveli – 627012, Tamilnadu, India ' Department of Banking Technology, School of Management, Pondicherry University, Pondicherry – 605014, Tamilnadu, India

Abstract: Texture defect detection can be defined as the process of determining the location and size of the collection pixels in a textured image which deviate in their intensity values or spatial in compression to a background texture. The detection of abnormalities is a very challenging problem in computer vision. In our proposed method we have designed a method for detecting the defect of pattern texture analysis. Initially, features are extracted from the input image using the grey level co-occurrence matrix (GLCM) and grey 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). The proposed pattern analysis showed that the traditional support vector machine is improved by means of kernel methods. In the final stage, the classified features are segmented using the modified fuzzy c means algorithm (MFCM).

Keywords: texture defect detection; preprocessing; grey level co-occurrence matrix; GLCM; grey level run-length matrix; GLRLM; improved support vector machine; ISVM; modified fuzzy c means; MFCM.

DOI: 10.1504/IJBIDM.2021.115475

International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.4, pp.411 - 434

Received: 21 Dec 2017
Accepted: 05 Oct 2018

Published online: 04 May 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article