scholarly journals Development of image texture analysis technique for boulder distribution measurements: applications to asteroids Ryugu and Itokawa

2021 ◽  
pp. 105249
Author(s):  
N. Tanabe ◽  
Y. Cho ◽  
E. Tatsumi ◽  
T. Ebihara ◽  
K. Yumoto ◽  
...  
2018 ◽  
Vol 24 (5) ◽  
pp. 471-477
Author(s):  
Clifford S. Todd ◽  
William A. Heeschen

AbstractA new method of image texture analysis is presented, based on the mean and standard deviation of gray levels within domains in an image. The calculations are performed recursively on domains of various sizes within the images. These gray level calculations are used as the input matrix for principal component analysis. The technique analyzes the entire image as a whole and is not for image segmentation. The analysis routine operates across all distances, frequencies and directions in the image, and is not computationally burdensome. The method was applied to scanning electron microscope images of reverse osmosis membranes on domains from 23 nm to 3 µm. The texture analysis technique performed well in identifying the surface morphology and, once calibrated, in predicting the surface roughness as measured by atomic force microscopy.


Author(s):  
Miroslav Benco ◽  
Patrik Kamencay ◽  
Robert Hudec ◽  
Martina Radilova ◽  
Peter Sykora

Measurement ◽  
2014 ◽  
Vol 47 ◽  
pp. 130-144 ◽  
Author(s):  
Samik Dutta ◽  
Kaustav Barat ◽  
Arpan Das ◽  
Swapan Kumar Das ◽  
A.K. Shukla ◽  
...  

2016 ◽  
Vol 18 (suppl_6) ◽  
pp. vi128-vi128
Author(s):  
Manabu Kinoshita ◽  
Hideyuki Arita ◽  
Toshiki Yoshimine ◽  
Masamichi Takahashi ◽  
Yoshitaka Narita ◽  
...  

2004 ◽  
Author(s):  
Umasankar Kandaswamy ◽  
Donald A. Adjeroh ◽  
M. C. Lee

2017 ◽  
Vol 24 (3) ◽  
pp. 1636-1645 ◽  
Author(s):  
Shuaibing Li ◽  
Guoqiang Gao ◽  
Guangcai Hu ◽  
Bo Gao ◽  
Tianshan Gao ◽  
...  

2014 ◽  
Vol 2 (3) ◽  
pp. 1-14
Author(s):  
Haotian Zhai ◽  
Hongbin Huang ◽  
Shaoyan He ◽  
Weiping Liu

Texture analysis plays an important role in image processing. In the field of texture analysis, the regular texture has been studied a lot, but the natural texture with complex backgrounds is less studied. This paper brings texture analysis into the study of rice paper's classification. First of all it shows the processing flow chart of rice paper classification. By comparing the different kinds of texture analysis methods it chooses the LAWS texture method and uncertainty texture spectrum method to achieve the rice paper classification. When it uses the two texture analysis methods separately, the classification accuracy of rice paper is lower, so it tries to combine the two texture analysis methods. The experimental results show that the classification result got with two combined texture analysis methods is better than that got with one single texture analysis method. The classification accuracy of rice paper has been distinctly improved after the combination of the two texture analysis methods.


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