Defect detection in textured materials using Gabor filters

By Ajay Kumar, Grantham Kwok-Hung Pang. Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129), 2000

497 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Industrial and Manufacturing Engineering, Polymers and Plastics

View Paper: https://www.semanticscholar.org/paper/aa873bb67cc952f89116e29073e34484c1ef4ef5

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In Atomic and Molecular Physics, and Optics

In Industrial and Manufacturing Engineering

In Polymers and Plastics

Citations in the corpus

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques (2008, background): “Later in [ 63 ], the same authors used a class of self-similar Gabor functions to classify fabric defects.”
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY (2020, methodology): “Kumar and Pang [91] used only real Gabor functions for fabric defect detection, and then, in [68], they used similar features on plain and twill fabrics in three schemes with no explicit results in the ﬁrst scheme, but with 100% accuracy in the second and third schemes.”
Texture Feature Extraction Methods: A Survey (2019, background): “In spite of the difficulty to give a precise definition of this notion, the analysis of texture is used in many applications: biomedical field (see [4]–[7]), industrial automation (see [8]–[10]), document image analysis (see [11]–[13]), remote sensing (see [14]–[16]), face recognition (see [17]) to ”
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, unknown): “(d) defect bigknot of textile materials [21].”
Steel defect classification with Max-Pooling Convolutional Neural Networks (2012, background): “and have been successfully applied for defect detection in textured materials in general [10], for textile flaw detection [11] and face recognition [12] in particular.”
Computer-Vision-Based Fabric Defect Detection: A Survey (2008, methodology): “and asymmetric Gabors filters has been detailed in [105], [106], [139] and [152], respectively.”
Automatic detection of welding defects using texture features (2003, background): “Gabor filter has a real and an imaginary component that are stored in M × M masks, called R pq and I pq respectively, where p = 1, ..., S, denotes the scale, and q = 1, ..., L, denotes the orientation (for details see (Kumar & Pang, 2002)).”