Defect Detection of Fabrics by Grey-Level Co-Occurrence Matrix and Artificial Neural Network

View Sample PDF

Author(s): Dilip k. Choudhury (GIET Gunupur, India)and Sujata Dash (North Orissa University, India)
Copyright: 2018
Pages: 13
Source title: Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
Source Author(s)/Editor(s): Sujata Dash (North Orissa University, India), B.K. Tripathy (VIT University, India)and Atta ur Rahman (University of Dammam, Saudi Arabia)
DOI: 10.4018/978-1-5225-2857-9.ch014

Keywords: Artificial Intelligence / Biologically-Inspired Computing / Information Science Reference / Medicine & Healthcare

Purchase

View Defect Detection of Fabrics by Grey-Level Co-Occurrence Matrix and Artificial Neural Network on the publisher's website for pricing and purchasing information.

Abstract

The class of Textiles produced from terephthalic acid and ethylene glycol by condensation polymerization has many end-uses for example these are used as filter fabric in railway track to prevent soil erosion, in cement industry these are used in boiler department as filter fabric to prevent the fly-ash from mixing in the atmosphere. Presently, the quality checking is done by the human in the naked eye. The automation of quality check of the non-Newtonian fabric can be termed as Image Analysis or texture analysis problem. A Simulation study was carried out by the process of Image Analysis which consists of two steps the former is feature extraction and the later part is recognition. Various techniques or tools that are presently studied in research for texture feature extraction are Grey level co-occurrence matrix(GLCM), Markov Random Field, Gabor filter. A GLCM matrix with 28 Haralick features were taken as input for this chapter.

The IRMA Community

Research IRM

Defect Detection of Fabrics by Grey-Level Co-Occurrence Matrix and Artificial Neural Network

Purchase

Abstract

Related Content

IRMA Sponsors