Structural Feature Extraction from Regular-Shaped Rigid Objects(2) - Enhanced by Prior Knowledge

2010 ◽  
Author(s):  
Lei Zhang ◽  
Qingshan Fu ◽  
Xingguo Zhang ◽  
Guoran Hua ◽  
Longbiao Zhu
Keyword(s):  
Feature Extraction ◽  
Structural Feature ◽  
Prior Knowledge ◽  
Rigid Objects

Structural feature extraction in online extrusion of plastics

1981 ◽  
Vol 5 (1) ◽  
pp. 13-18
Author(s):  
David Barkley ◽  
Charles McCorkell ◽  
Mustafa Akay
Keyword(s):  
Feature Extraction ◽  
Structural Feature

scholarly journals Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography

2021 ◽  
Vol 15 ◽  
Author(s):  
Dongwei Chen ◽  
Rui Miao ◽  
Zhaoyong Deng ◽  
Na Han ◽  
Chunjian Deng
Keyword(s):  
Feature Extraction ◽  
Prior Knowledge ◽  
Granger Causality ◽  
Affective Computing ◽  
Causality Analysis ◽  
Emotion Classification ◽  
Analysis Model ◽  
Granger Causality Analysis ◽  
Model Based ◽  
Eeg Data

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L1/2 norm framework for feature extraction, and uses L2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46–21.81%.

scholarly journals A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Hong Yang ◽  
Yasheng Zhang ◽  
Wenzhe Ding
Keyword(s):  
Feature Extraction ◽  
Structural Feature ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
Feature Space ◽  
Image Feature ◽  
Learning Method ◽  
Sample Data ◽  
Low Dimensional ◽  
Space Target

Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the longest axis of the ISAR image to generate the global trace feature of the space target ISAR image. By introducing the local structural feature, Local Binary Pattern (LBP), the complementary fusion of the global and local features is achieved, which makes up for the missing structural information of the trace feature and ensures the integrity of the ISAR image feature information. The representation of trace and LBP features in a low-dimensional mapping feature space is found by using the manifold learning method. Under the condition of maintaining the local neighborhood relationship in the original feature space, the effective fusion of trace and LBP features is achieved. So, in the practical application process, the target recognition accuracy is no longer affected by trace function, LBP feature block number selection, and other factors, realizing the high robustness of the algorithm. To verify the effectiveness of the proposed algorithm, an ISAR image database containing 1325 samples of 5 types of space targets is used for experiments. The results show that the classification accuracy of the 5 types of space targets can reach more than 99%, and the recognition accuracy is no longer affected by the trace feature and LBP feature selection, which has strong robustness. The proposed method provides a fast and effective high-precision model for space target feature extraction, which can give some references for solving the problem of space object efficient identification under the condition of small sample data.

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