Automatic hip geometric feature extraction in DXA imaging using regional random forest

2019 ◽  
Vol 27 (2) ◽  
pp. 207-236 ◽  
Author(s):  
Dildar Hussain ◽  
Seung-Moo Han ◽  
Tae-Seong Kim
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Geometric Feature ◽  
Geometric Feature Extraction

Geometric Feature Extraction of Ship in High-resolution Synthetic Aperture Radar Images

2018 ◽  
Vol 47 (1) ◽  
pp. 110001
Author(s):  
熊伟 XIONG Wei ◽  
徐永力 XU Yong-li ◽  
崔亚奇 CUI Ya-qi ◽  
李岳峰 LI Yue-feng
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Geometric Feature ◽  
Radar Images ◽  
Geometric Feature Extraction ◽  
Aperture Radar

Geometric feature extraction by FTAs for finger‐based biometrics system

2016 ◽  
Vol 6 (3) ◽  
pp. 157-164 ◽  
Author(s):  
Mohd Shahrimie Mohd Asaari ◽  
Shahrel Azmin Suandi ◽  
Bakhtiar Affendi Rosdi
Keyword(s):  
Feature Extraction ◽  
Geometric Feature ◽  
Geometric Feature Extraction

Research of Face Identification Method Based on Geometric Feature Extraction and the Enlargement of Image Interpolation with Scientific Image Materials

2011 ◽  
Vol 63-64 ◽  
pp. 846-849
Author(s):  
Jian Ni ◽  
Yu Duo Li
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Interpolation Method ◽  
Image Interpolation ◽  
Geometric Feature ◽  
Face Identification ◽  
Human Face ◽  
Training Experience ◽  
Geometric Feature Extraction ◽  
Vector Weight

To achieve human face identification, this paper adopts the method of geometric feature extraction and the enlargement of image interpolation on the basis of the completion of face detection. First of all, the input digital image will be normalized to reduce the complexity of the image, and then the feature of human face will be extract. With the feature information extracted, we can construct the feature vector and assign different weights to different feature vector. Weight is interpreted as the EXP obtained after a large amount of training experience is gained. Finally, to get the similarity of picture, the bilinear interpolation method is adopted on the basis of the nearest interpolation. Thus, we will get the results of face identification according to the similarity quality. Through the development and implementation of practical programming, this paper proves the feasibility of such method.

scholarly journals Geometric Feature Extraction by a Multimarked Point Process

2010 ◽  
Vol 32 (9) ◽  
pp. 1597-1609 ◽  
Author(s):  
Florent Lafarge ◽  
Georgy Gimel'farb ◽  
Xavier Descombes
Keyword(s):  
Feature Extraction ◽  
Point Process ◽  
Geometric Feature ◽  
Geometric Feature Extraction
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