scholarly journals Seam welds quality control improvement using method of pseudo-color coding images

2021 ◽  
Vol 2094 (2) ◽  
pp. 022058
Author(s):  
M F Noskov
Keyword(s):  
Quality Control ◽  
Color Image ◽  
Color Coding ◽  
Visual Examination ◽  
X Ray ◽  
Black And White ◽  
Optical Mixing ◽  
Pseudo Color ◽  
Ray Methods ◽  
Methods Of Control

Abstract The method of seam welds quality control using X-ray is considered. The X-ray methods of control are based on the capability of gamma radiation to penetrate through a metal including welded areas. Regions having defects - pores, faulty welds, cracks, scale inclusions - look darker on images. Appearance, linear dimensions and depths of the defects usually are determined by a visual examination comparing the X-ray image with standard defects images. It is known that a human eye can distinguish not more than 12-15 shades on a black and white image but more than a hundred on a colored image. The paper considers possibilities of the developed method by the author and based on the optical mixing of two or three complementary colors - red, blue and green. The method can use only one pair of the colors at a time, i.e. it is possible to have three various pairs for a pseudo-color image. The obtained pseudo-color image has the same informational capacity as the original black and white image. But the greater fraction of the saved information becomes available for visual examination of the X-ray image. In the end the efficiency of the seam weld quality control increases.

Dual X-ray absorptiometry quality control: Comparison of visual examination and process-control charts

2009 ◽  
Vol 11 (5) ◽  
pp. 626-637 ◽  
Author(s):  
Ying Lu ◽  
Ashwini K. Mathur ◽  
Barbara A. Blunt ◽  
Claus C. Glüer ◽  
A. Steve Will ◽  
...  
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Control Charts ◽  
Visual Examination ◽  
X Ray ◽  
Process Control Charts ◽  
Control Comparison

scholarly journals Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2258
Author(s):  
Madhab Raj Joshi ◽  
Lewis Nkenyereye ◽  
Gyanendra Prasad Joshi ◽  
S. M. Riazul Islam ◽  
Mohammad Abdullah-Al-Wadud ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
User Study ◽  
Mean Squared Error ◽  
Color Image ◽  
Machine Learning Techniques ◽  
Global Features ◽  
Black And White ◽  
Historical Images ◽  
Learning Techniques

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

Synchrotron X-Ray Topography Study on the Relationship between Local Basal Plane Bending and Basal Plane Dislocations in PVT-Grown 4H-SiC Substrate Wafers

2020 ◽  
Vol 1004 ◽  
pp. 393-400
Author(s):  
Tuerxun Ailihumaer ◽  
Hongyu Peng ◽  
Balaji Raghothamachar ◽  
Michael Dudley ◽  
Gilyong Chung ◽  
...  
Keyword(s):  
Basal Plane ◽  
Local Strain ◽  
Grazing Incidence ◽  
Peak Shift ◽  
Linear Mapping ◽  
Incidence Geometry ◽  
Convex Shape ◽  
X Ray ◽  
Black And White ◽  
Plane Bending

Synchrotron monochromatic beam X-ray topography (SMBXT) in grazing incidence geometry shows black and white contrast for basal plane dislocations (BPDs) with Burgers vectors of opposite signs as demonstrated using ray tracing simulations. The inhomogeneous distribution of these dislocations is associated with the concave/convex shape of the basal plane. Therefore, the distribution of these two BPD types were examined for several 6-inch diameter 4H-SiC substrates and the net BPD density distribution was used for evaluating the nature and magnitude of basal plane bending in these wafers. Results show different bending behaviors along the two radial directions - [110] and [100] directions, indicating the existence of non-isotropic bending. Linear mapping of the peak shift of the 0008 reflection along the two directions was carried out using HRXRD to correlate with the results from the SMBXT measurements. Basal-plane-tilt angle calculated using the net BPD density derived from SMBXT shows a good correlation with those obtained from HRXRD measurements, which further confirmed that bending in basal plane is caused by the non-uniform distribution of BPDs. Regions of severe bending were found to be associated with both large tilt angles (95% black contrast BPDs to 5% white contrast BPDs) and abrupt changes in a and c lattice parameters i.e. local strain.

Scattering and phase-contrast X-ray methods reveal damage to glass fibers in endodontic posts following dental bur trimming

2020 ◽  
Author(s):  
Ana Prates Soares ◽  
Daniel Baum ◽  
Bernhard Hesse ◽  
Andreas Kupsch ◽  
Bernd R. Müller ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Glass Fibers ◽  
X Ray ◽  
Ray Methods
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