Defect detection in magnetic tile images based on stationary wavelet transform

Yang, Chengli; Liu, Peiyong; Yin, Guofu; Jiang, Honghai; Li, Xueqin

doi:10.1016/j.ndteint.2016.04.006

Cited by 56 publications

(30 citation statements)

References 25 publications

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“…Then, the low-pass filter and nonlinear improvements were used to eliminate the interference and improve the target in the subbands produced by Wavelet Transform. As the result shows, the proposed method achieves an accuracy rate of 92.86% in the detection of several surface defects [14].…”

Section: Related Workmentioning

confidence: 87%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

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A set of images of metal pieces with different finishes was obtained for use as a standard reference in the estimate of the surface roughness (Ra) through the fractal dimension (D) of the power spectrum and the spectrum of intensities. A system of artificial vision with two sources of LED light illumination (white and red) and three angles of incidence was used. The best results were found with the spectra of intensities regardless of the type of lighting or the angle of incidence. The values of the fractal dimension were correlated with the surface roughness values obtained with a contact profilometer to build regression curves that are used to estimate the surface roughness on site with a statistical error under 5%. This system could be used as an inspection station to reduce waiting times and unnecessary transport (Poka Yoke System).

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Section: Related Workmentioning

confidence: 87%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

View full text Add to dashboard Cite

show abstract

“…For a given source image I ( x , y ) of size m × n , j th level discrete SWT decomposition is given as follows, 35

A^{j + 1} ((),, a_{x}, a_{y}) = \sum_{k_{x = - \infty}}^{k_{x = \infty}} \sum_{k_{y = - \infty}}^{k_{y = \infty}} h_{k_{x}}^{j} h_{k_{y}}^{j} C^{j} ((),, a_{x} + k_{x}, a_{y} + k_{y})

D_{h}^{j + 1} ((),, a_{x}, a_{y}) = \sum_{k_{x = - \infty}}^{k_{x = \infty}} \sum_{k_{y = - \infty}}^{k_{y = \infty}} g_{k_{x}}^{j} h_{k_{y}}^{j} C^{j} ((),, a_{x} + k_{x}, a_{y} + k_{y})

D_{v}^{j + 1} ((),, a_{x}, a_{y}) = \sum_{k_{x = - \infty}}^{k_{x = \infty}} \sum_{k_{y = - \infty}}^{k_{y = \infty}} {h_{k_{x}}^{j} g}_{k_{y}}^{j} C^{j} ((),, a_{x} + k_{x}, a_{y} + k_{y})

D_{d}^{j + 1} ((),, a_{x}, a_{y}) = \sum_{k_{x = - \infty}}^{k_{x ...}}

…”

Section: Methodsmentioning

confidence: 99%

Multistage multimodal medical image fusion model using feature‐adaptive pulse coupled neural network

Singh¹,

Gupta

2020

Int J Imaging Syst Tech

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Medical image fusion focuses to fuse complementary diagnostic details for better visualization of comprehensive information and interpretation of various diseases and its treatment planning. In this paper, a multistage multimodal fusion model is presented based on nonsubsampled shearlet transform (NSST), stationary wavelet transform (SWT), and feature‐adaptive pulse coupled neural network. Firstly, NSST is employed to decompose the source images into optimally sparse multi‐resolution components followed by SWT. Secondly, structural features are extracted by a weighted sum‐modified Laplacian and applied to an adaptive model to map feature weights for low‐band SWT component fusion, and texture feature‐based fusion rule is applied to fuse high‐band SWT components. High‐frequency NSST components are fused using the absolute maximum and sum of absolute difference based rule to retain complex directional details. Experimental results show that the proposed method obtains significantly better fused medical images compared to others with excellent visual quality and improved computational measures.

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“…Texture features extracted from wavelet decomposition images are widely used for texture classification and segmentation [8], [9]. Yang et al [10] proposed a method of using stationary wavelet transform to detect low contrast defects in magnetic tile images under various illuminations. Yao et al [11] The multi-scale wavelet representation is used to obtain the features of the input image.…”

Section: Introductionmentioning

confidence: 99%

Weak Micro-Scratch Detection Based on Deep Convolutional Neural Network

et al. 2019

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Metal component surfaces are random textured and non-smooth. There are many stains on the surface of metal component that are similar to the gray scale of the scratches. The scratches have non-uniform gray distribution, various shapes, and low contrast in their background, posing challenges in accurate scratch detection. This paper presents a method for detecting weak scratches on metal component surfaces based on deep convolutional neural networks (DCNNs). First, a DCNN is trained using labeled scratch images. Then, the scratches and some faults are detected by the trained DCNN, and most of the faults can be removed through properly thresholding based on the size of connected regions. Finally, the scratch length united in the number of pixels is obtained by the skeleton extraction. The experimental results show that the proposed method can effectively deal with background noise, thereby achieving accurate scratch detection. INDEX TERMS Deep convolutional neural network, scratch, machine vision.

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Defect detection in magnetic tile images based on stationary wavelet transform

Cited by 56 publications

References 25 publications

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Multistage multimodal medical image fusion model using feature‐adaptive pulse coupled neural network

Weak Micro-Scratch Detection Based on Deep Convolutional Neural Network

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