scholarly journals Building Materials Defect Monitoring Based on Digital Image Recognition Technology

2018 ◽  
Vol 128 ◽  
pp. 012026
Author(s):  
Zhe Hu
Keyword(s):  
Image Recognition ◽  
Digital Image ◽  
Building Materials

scholarly journals Evaluation of Digital Image Recognition Methods for Mass Spectrometry Imaging Data Analysis

2018 ◽  
Vol 29 (12) ◽  
pp. 2467-2470 ◽  
Author(s):  
Måns Ekelöf ◽  
Kenneth P. Garrard ◽  
Rika Judd ◽  
Elias P. Rosen ◽  
De-Yu Xie ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Image Recognition ◽  
Digital Image ◽  
Mass Spectrometry Imaging ◽  
Imaging Data

Improved Algorithm for Wavelet Threshold Denoising and Application in Crack Image Recognition

2014 ◽  
Vol 945-949 ◽  
pp. 1846-1850
Author(s):  
Hai Biao Li ◽  
Xin Xia
Keyword(s):  
Image Recognition ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Threshold Value ◽  
Image Noise ◽  
Threshold Function ◽  
Determination Method ◽  
Signal To Noise ◽  
Visual Effects ◽  
Edge Preservation

In crack image recognition, Donoho’s universal wavelet threshold de-noising method appears "over-kill" phenomenon due to the lack of self-adaptability of threshold value; hence the image may lose its edge details. To handle this problem, the Donoho’s universal threshold and threshold function are improved and an adaptive determination method of threshold coefficient is introduced in this paper. Experimental results shows that the proposed method can effectively remove digital image noise and achieve a better edge protection, higher edge preservation index, better visual effects and higher peak signal-to-noise ratio.

Fractional Fourier-Radial Transform for Digital Image Recognition

2020 ◽  
Vol 93 (1) ◽  
pp. 49-66
Author(s):  
Luis Felipe López-Ávila ◽  
Josué Álvarez-Borrego ◽  
Selene Solorza-Calderón
Keyword(s):  
Image Recognition ◽  
Digital Image

scholarly journals Digital image recognition based on improved cognitive neural network

2019 ◽  
Vol 10 (1) ◽  
pp. 125-128 ◽  
Author(s):  
Yuxi Liu
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Digital Image ◽  
High Accuracy ◽  
Not Given ◽  
Mixed Signal ◽  
Neural Network Method ◽  
Innovative Method ◽  
Network Method ◽  
Source Signal

Abstract This paper presents an innovative cognitive neural network method application in digital image recognition. The following conclusion can be drawn. Each point of the graph is transformed, and the original color of the transformed new coordinates is given to the point. If after all the points have transformed, if there is a point and no point has converted to this point, the point is not given a color. Then this point will form a hole or a stripe, and the color is the color of the point initialization. The innovative method can effectively separate the digital image recognition signal from the mixed signal and maintain the waveform of the source signal with high accuracy, thus laying the foundation for the next step of recognition.

scholarly journals Developing a semantic image model using machine learning based on convolutional neural networks

2020 ◽  
pp. 352-361
Author(s):  
P.I. Andon ◽  
◽  
A.M. Glybovets ◽  
V.V. Kuryliak ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Digital Image ◽  
Learning Model ◽  
Semantic Model ◽  
Image Model ◽  
Automatic Construction ◽  
Machine Learning Model

This paper describes the main areas of research in the field of developing computer models for the automatization of digital image recognition. The concept of the semantic image model is introduced and the implementation of the machine learning model for solving the problem of automatic construction of such a model is described. The semantic model consists of a list of objects represented in the image and their relationships. The developed model was compared to other solutions and showed better results in all but one case. The performance of the model is justified by the use of the latest achievements of machine learning, including ZNM, TL, Faster R-CNN, and VGG16. Much of the links represented in the image are spatial links, so for the model to work better, you need to use that fact in designing it, which was done.

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