Fractional-Order Embedding Supervised Canonical Correlations Analysis with Applications to Feature Extraction and Recognition

2016 ◽  
Vol 45 (1) ◽  
pp. 279-297 ◽  
Author(s):  
Hong-Kun Ji ◽  
Quan-Sen Sun ◽  
Yun-Hao Yuan ◽  
Ze-Xuan Ji
Keyword(s):  
Feature Extraction ◽  
Fractional Order ◽  
Canonical Correlations

A Fractional-order Sobel Operator for Medical Image Structure Feature Extraction

2013 ◽  
Vol 860-863 ◽  
pp. 2910-2913 ◽  
Author(s):  
Dan Tian ◽  
Jing Fei Wu ◽  
Ya Jie Yang
Keyword(s):  
Feature Extraction ◽  
Fractional Order ◽  
Medical Image ◽  
Order Operator ◽  
Visual Effects ◽  
Image Structure ◽  
First Order ◽  
Structure Feature ◽  
Sobel Operator ◽  
Edge Points

This paper proposes a novel fractional-order gradient operator for medical image structure feature extraction. The proposed operator can be seen as generalization of the first-order Sobel operator. The goal is to utilize the frequency characteristic of the fractional derivative for extracting more structure feature details. A thresholding is set based on the average fractional-order gradient for marking the edge points, and then the image structure can be extracted. Experiments show that the proposed fractional-order operator yields good visual effects.

Laplacian multiset canonical correlations for multiview feature extraction and image recognition

2015 ◽  
Vol 76 (1) ◽  
pp. 731-755 ◽  
Author(s):  
Yun-Hao Yuan ◽  
Yun Li ◽  
Xiao-Bo Shen ◽  
Quan-Sen Sun ◽  
Jin-Long Yang
Keyword(s):  
Feature Extraction ◽  
Image Recognition ◽  
Canonical Correlations
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