Training spiking neural networks with the improved grey-level co-occurrence matrix algorithm for texture analysis

2015 ◽  
Author(s):  
Zhenmin Zhang ◽  
Qingxiang Wu ◽  
Xuan Wang ◽  
Qiyan Sun
Keyword(s):  
Neural Networks ◽  
Texture Analysis ◽  
Spiking Neural Networks ◽  
Grey Level ◽  
Matrix Algorithm ◽  
Occurrence Matrix

scholarly journals Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shrouq H. Aleithan ◽  
Doaa Mahmoud-Ghoneim
Keyword(s):  
Neural Networks ◽  
Texture Analysis ◽  
Higher Order ◽  
Image Texture ◽  
Proper Class ◽  
Automated Classification ◽  
Linear Discriminant ◽  
Ann Classifier ◽  
Occurrence Matrix

AbstractThe need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS2 and WS2, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS2 and WS2, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class.

Texture Analysis of Turned Surface Images Using Grey Level Co-Occurrence Technique

2011 ◽  
Vol 365 ◽  
pp. 38-43 ◽  
Author(s):  
Anurup Datta ◽  
Samik Dutta ◽  
Surjya K. Pal ◽  
Ranjan Sen ◽  
Sudipta Mukhopadhyay
Keyword(s):  
Surface Roughness ◽  
Texture Analysis ◽  
Image Texture ◽  
Analysis Method ◽  
Grey Level ◽  
Machined Surface ◽  
Image Texture Analysis ◽  
Pixel Pair ◽  
Occurrence Matrix

The main purpose of this work was to study the applicability of an image texture analysis method, namely, the grey level co-occurrence matrix (GLCM) method for the examination of the smoothness of the images of a turned surface. The effect of the variation of the pixel pair spacing (pps) on the construction of the GLCM was also considered and then, contrast and homogeneity were calculated from the GLCMs which served as texture descriptors for the quality of the machined surface. Finally, the variation of these texture descriptors with cutting time was analyzed and compared with the variation of tool wear and surface roughness with cutting time.

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