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3D Research

, 10:5 | Cite as

Fabric Defect Detection Adopting Combined GLCM, Gabor Wavelet Features and Random Decision Forest

  • Nilesh Tejram DeotaleEmail author
  • Tanuja K. Sarode
3DR Express
  • 7 Downloads

Abstract

In image analysis and pattern recognition activity, one of the most salient characteristics is texture. The global region of images in spatial domain has an enhanced processing effect with the help of co-occurrence matrix and in the frequency domain for the admirable performance such as multi-scale, multi-direction local information is obtained from Gabor wavelet. The consolidation of gray-level co-occurrence matrix and Gabor wavelet is utilized to fabric image feature texture eradication. In classification phase, random decision forest (RDFs) Classifier is applied to classify the input fabric image into defective or non-defective. RDFs are a novel and outfit machine learning strategy which fuses the element choice. Nevertheless, RDFs exhibit a lot of advantages when compared with other modeling approaches within the category. The main advantages are, RDFs can handle both the continuous and discrete variables, RDFs does not overfit as a classifier, and run quick and productively when taking care of expansive datasets.

Graphical Abstract

In this paper the consolidation of gray-level co-occurrence matrix (GLCM) and Gabor wavelet is utilized to fabric image feature texture eradication. In classification phase, random decision forest (RDFs) classifier is applied to classify the input fabric image into defective or non-defective.

Keywords

Fabric defect detection Feature extraction GLCM Gabor wavelet Random decision forest 

Abbreviations

GLCM

Gray-level co-occurrence matrix

RDFs

Random decision forest

2D

Two-dimensional

LVQ

Learning vector quantization

GGD

Generalized Gaussian density

ML

Maximum likelihood

ANN

Artificial neural network

MRF

Markov random field

LCP

Local comprehensive patterns

ILS

Isotropic lattice segmentation

LSG

Lattice segmentation assisted by Gabor filters

MCA

Morphological component analysis

VOV

Variance of variance

LMGCP

Local multi-channels Gabor comprehensive patterns

LGMM

Local Gabor magnitude map

DDP

Direction derivatives patterns

DMP

Direction magnitude patterns

AMF

Adaptive median filter

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Copyright information

© 3D Display Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Engineering DepartmentThadomal Shahani College of EngineeringMumbaiIndia

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