Application of Undecimated Wavelet Transform to Surface Defect Detection of Hot Rolled Steel Plates

By Xiu-yong Wu, Ke Xu, Jin-wu Xu. International Congress on Image and Signal Processing, 2008

48 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/29fb1657ef40f66f296d9661085f6f75d053f366

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Mohammad Reza Jahanshahi
2. Fu-Chen Chen
3. Nirbhar Neogi
4. Pranab Kumar Dutta
5. Dusmanta Kumar Mohanta
6. Chunhua Yang

In Electrical and Electronic Engineering

1. Nirbhar Neogi
2. Mohammad Reza Jahanshahi
3. Pranab Kumar Dutta
4. Dusmanta Kumar Mohanta
5. Fu-Chen Chen

In Industrial and Manufacturing Engineering

1. Mohammad Reza Jahanshahi
2. Fu-Chen Chen
3. Nirbhar Neogi
4. Pranab Kumar Dutta
5. Dusmanta Kumar Mohanta
6. Billie F. Spencer Jr.
7. R. Thomas
8. Vedhus Hoskere

Citations in the corpus

• Review of vision-based steel surface inspection systems (2014, methodology): “In a large number of instances [3,35,41,42,56,58,61], it has been possible to localise RoIs with a high degree of accuracy.”
• NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion (2018, methodology): “For metallic surfaces, vision-based crack detection methods [13], [14] have been reviewed in [3] for steel surface inspection during production.”
• Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020, background): “[74] proposed an undecimated wavelet transform (UMT) for solving the problem of false alarms resulted from oxidized scales and water marks with an overall recognition rate of 90.”