Multi-sample 3D face registration based on TPS transformation and linear combination model

2011 ◽  
Author(s):  
Yongli Hu ◽  
Xianghui Zhu ◽  
Yanfeng Sun ◽  
Baocai Yin
Keyword(s):  
Linear Combination ◽  
3D Face ◽  
Combination Model ◽  
Linear Combination Model

Prediction of Clock Errors of Atomic Clocks Based on Modified Linear Combination Model†

2011 ◽  
Vol 35 (3) ◽  
pp. 318-326 ◽  
Author(s):  
Wang Ji-gang ◽  
Hu Yong-hui ◽  
He Zai-min ◽  
Wu Jian-feng ◽  
Ma Hong-jiao ◽  
...  
Keyword(s):  
Linear Combination ◽  
Atomic Clocks ◽  
Combination Model ◽  
Linear Combination Model

scholarly journals THIN CLOUD DETECTION METHOD BY LINEAR COMBINATION MODEL OF CLOUD IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 1079-1083 ◽  
Author(s):  
L. Liu ◽  
J. Li ◽  
Y. Wang ◽  
Y. Xiao ◽  
W. Zhang ◽  
...  
Keyword(s):  
Linear Combination ◽  
Detection Method ◽  
Image Features ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Surface Information ◽  
Combination Model ◽  
Detection Program ◽  
Adaboost Classifier ◽  
Linear Combination Model

The existing cloud detection methods in photogrammetry often extract the image features from remote sensing images directly, and then use them to classify images into cloud or other things. But when the cloud is thin and small, these methods will be inaccurate. In this paper, a linear combination model of cloud images is proposed, by using this model, the underlying surface information of remote sensing images can be removed. So the cloud detection result can become more accurate. Firstly, the automatic cloud detection program in this paper uses the linear combination model to split the cloud information and surface information in the transparent cloud images, then uses different image features to recognize the cloud parts. In consideration of the computational efficiency, AdaBoost Classifier was introduced to combine the different features to establish a cloud classifier. AdaBoost Classifier can select the most effective features from many normal features, so the calculation time is largely reduced. Finally, we selected a cloud detection method based on tree structure and a multiple feature detection method using SVM classifier to compare with the proposed method, the experimental data shows that the proposed cloud detection program in this paper has high accuracy and fast calculation speed.

Sign in / Sign up

Export Citation Format

Share Document