scholarly journals Unsupervised Multi-modality Registration Network Based on Spatially Encoded Gradient Information

2022 ◽  
pp. 151-159
Author(s):  
Wangbin Ding ◽  
Lei Li ◽  
Liqin Huang ◽  
Xiahai Zhuang
Keyword(s):  
Gradient Information

Moving Target Detection Based on Principle of Connectivity and Texture Gradient

2012 ◽  
Vol 605-607 ◽  
pp. 2117-2120
Author(s):  
Min Huang ◽  
Yang Zhang ◽  
Gang Chen ◽  
Guo Feng Yang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Texture Gradient ◽  
Moving Target ◽  
Moving Target Detection ◽  
Gradient Information ◽  
Target Segmentation ◽  
Target Profile

In target detection, “hole” phenomenon is present in the detection result, and the shadow is difficult to remove. To solve these problems, we propose a target detection algorithm based on principle of connectivity and texture gradient. Firstly, we use the connectivity principle to find the largest target prospects connection area to get a complete target contour, secondly we use target texture gradient information to further remove the shadow of the target. At last, the experimental results show that the algorithm can obtain a clear target profile and improve the accuracy of the moving target segmentation.

scholarly journals High dimensional Kriging metamodelling utilising gradient information

2016 ◽  
Vol 40 (9-10) ◽  
pp. 5256-5270 ◽  
Author(s):  
S. Ulaganathan ◽  
I. Couckuyt ◽  
T. Dhaene ◽  
J. Degroote ◽  
E. Laermans
Keyword(s):  
High Dimensional ◽  
Gradient Information

Medical volume rendering based on gradient information

2015 ◽  
pp. 181-186 ◽  
Author(s):  
Thiago de Moraes ◽  
Paulo Amorim ◽  
Jorge da Silva ◽  
Helio Pedrini ◽  
Maria Meurer
Keyword(s):  
Volume Rendering ◽  
Gradient Information

Polynomial-Chaos–Kriging with Gradient Information for Surrogate Modeling in Aerodynamic Design

AIAA Journal ◽  
2021 ◽  
pp. 1-18
Author(s):  
Lavi R. Zuhal ◽  
Kemas Zakaria ◽  
Pramudita Satria Palar ◽  
Koji Shimoyama ◽  
Rhea Patricia Liem
Keyword(s):  
Polynomial Chaos ◽  
Surrogate Modeling ◽  
Aerodynamic Design ◽  
Gradient Information
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