Inconspicuous Adversarial Perturbation Post-processing Method with Image Texture Analysis

2021 ◽  
Vol 19 (2) ◽  
pp. 89-95
Author(s):  
Jung-Hun Kim ◽  
Young-Mo Kim ◽  
Doo-Hyun Choi
Keyword(s):  
Texture Analysis ◽  
Processing Method ◽  
Image Texture ◽  
Post Processing ◽  
Image Texture Analysis

Image Texture Analysis based on Color Level Methods using Neural Network

2020 ELEKTRO ◽  
2020 ◽  
Author(s):  
Miroslav Benco ◽  
Patrik Kamencay ◽  
Robert Hudec ◽  
Martina Radilova ◽  
Peter Sykora
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Image Texture ◽  
Image Texture Analysis

Characterization of micrographs and fractographs of Cu-strengthened HSLA steel using image texture analysis

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

scholarly journals NIMG-21. IMPACT OF GENETIC ALTERATIONS ON TUMOR LOCATIONS AND LESION HETEROGENEITY FOR WHO GRADE 2 AND 3 GLIOMAS: A VOXEL-BASED LESION MAPPING AND IMAGE TEXTURE ANALYSIS OF 201 GLIOMAS

2016 ◽  
Vol 18 (suppl_6) ◽  
pp. vi128-vi128
Author(s):  
Manabu Kinoshita ◽  
Hideyuki Arita ◽  
Toshiki Yoshimine ◽  
Masamichi Takahashi ◽  
Yoshitaka Narita ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Genetic Alterations ◽  
Image Texture ◽  
Who Grade ◽  
Image Texture Analysis ◽  
Lesion Mapping

An efficient method for image texture analysis

2004 ◽  
Author(s):  
Umasankar Kandaswamy ◽  
Donald A. Adjeroh ◽  
M. C. Lee
Keyword(s):  
Texture Analysis ◽  
Efficient Method ◽  
Image Texture ◽  
Image Texture Analysis

Aging feature extraction of oil-impregnated insulating paper using image texture analysis

2017 ◽  
Vol 24 (3) ◽  
pp. 1636-1645 ◽  
Author(s):  
Shuaibing Li ◽  
Guoqiang Gao ◽  
Guangcai Hu ◽  
Bo Gao ◽  
Tianshan Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Texture Analysis ◽  
Image Texture ◽  
Image Texture Analysis ◽  
Insulating Paper

Rice Paper Classification Study Based on Signal Processing and Statistical Methods in Image Texture Analysis

2014 ◽  
Vol 2 (3) ◽  
pp. 1-14
Author(s):  
Haotian Zhai ◽  
Hongbin Huang ◽  
Shaoyan He ◽  
Weiping Liu
Keyword(s):  
Texture Analysis ◽  
Classification Accuracy ◽  
Image Texture ◽  
Analysis Method ◽  
Classification Result ◽  
Image Texture Analysis ◽  
Analysis Methods ◽  
Paper Classification ◽  
Regular Texture ◽  
Better Than

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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