Reversible image watermarking based on texture analysis of grey level co-occurrence matrix

2019 ◽  
Vol 19 (1) ◽  
pp. 83
Author(s):  
Shu zhi Li ◽  
Qin Hu ◽  
Xiao hong Deng ◽  
Zhao quan Cai
Keyword(s):  
Texture Analysis ◽  
Image Watermarking ◽  
Grey Level ◽  
Occurrence Matrix ◽  
Reversible Image Watermarking

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.

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

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