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133158

A Computer Vision System for Detecting Tufted Carpet Defects.

Article

Last updated: 22 Jan 2023

OverviewSimilar Items

Subjects

-

Tags

Textile Engineering

Abstract

This work presents an approach to extract, analyze and select image attributes necessary for building a vision computer system that is able to detect and classify tufted carper defects. Four different tufted carpets and four different defects were considered; missing pile, higher pile, lower pile, slobs and knots. Carpet digital images were statistically analyzed to calculate Mean, Variance, Skewness, Kurtosis, and Entropy, known as tonal features. Texture features were extracted from co-occurrence matrices describing the relationships between intensities of two pixels at a certain distance and angle from each other and evaluated using SGLDM and GLDM statistics. Graphical presentation and visual assessment were made to choose the most significant features. For classification. artificial neural networks were built and trained using perceptron and back propagation algorithms. The recognition was successful in detecting common tufted carpet defects. 

DOI

10.21608/bfemu.2020.133158

Authors

First Name

Ramsis

Last Name

Farag

MiddleName

-

Affiliation

Textile Engineering Department., Faculty of Engineering, El-Mansoura University., Egypt

Email

-

City

Mansoura

Orcid

-

First Name

Faiez

Last Name

Arid

MiddleName

-

Affiliation

Textile Engineering Department., Faculty of Engineering, El-Mansoura University., Egypt

Email

-

City

Mansoura

Orcid

-

First Name

Nadia

Last Name

Bondok

MiddleName

-

Affiliation

Labor University. Ras-Elbar, Damiettat, Egypt

Email

-

City

Damiettat

Orcid

-

Volume

29

Article Issue

1

Related Issue

19655

Issue Date

2004-03-01

Receive Date

2004-01-11

Publish Date

2020-12-28

Page Start

1

Page End

14

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 29_Issue 1_Pages 1-14.pdf

PDF . 369.0kB

Link

https://bfemu.journals.ekb.eg/article_133158.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=133158

Order

23

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023