scholarly journals Multi-Feature Fusion: A Driver-Car Matching Model Based on Curve Comparison

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 83526-83535
Author(s):  
Xianwei Meng ◽  
Hao Fu ◽  
Guiquan Liu ◽  
Lei Zhang ◽  
Yang Yu ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Matching Model ◽  
Model Based

Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm

2018 ◽  
Vol 55 (4) ◽  
pp. 1151-1169 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Guan-Rong Zeng ◽  
Ling-Yun He ◽  
Ya Meng
Keyword(s):  
Genetic Algorithm ◽  
Pattern Matching ◽  
Oil Price ◽  
Short Term ◽  
Matching Model ◽  
Model Based

scholarly journals Joint Event Extraction Model based on Multi-feature Fusion

2020 ◽  
Vol 174 ◽  
pp. 115-122
Author(s):  
Wang Shuo ◽  
Rao Yuan ◽  
Fan Xiaobing ◽  
Qi Jiangnan
Keyword(s):  
Feature Fusion ◽  
Event Extraction ◽  
Model Based ◽  
Joint Event ◽  
Extraction Model

scholarly journals Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2887 ◽  
Author(s):  
Linsheng Huang ◽  
Taikun Li ◽  
Chuanlong Ding ◽  
Jinling Zhao ◽  
Dongyan Zhang ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Disease Severity ◽  
Feature Fusion ◽  
Texture Features ◽  
Effective Control ◽  
Spectral Features ◽  
Head Blight ◽  
Color Feature ◽  
Model Based ◽  
Fusion Features

Fusarium head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed based on the fusion features of image and spectral features. First, the hyperspectral image of FHB infected in the range of the 400–1000 nm spectrum was collected, and the color parameters of wheat ear and spot region were segmented based on image features. Twelve sensitive bands were extracted using the successive projection algorithm, gray-scale co-occurrence matrix, and RGB color model. Four texture features were extracted from each feature band image as texture variables, and nine color feature variables were extracted from R, G, and B component images. Texture features with high correlation and color features were selected to participate in the final model building parameters via correlation analysis. Finally, the particle swarm optimization support vector machine (PSO-SVM) algorithm was used to build the model based on the diagnosis model of disease severity of FHB with different combinations of characteristic variables. The experimental results showed that the PSO-SVM model based on spectral and color feature fusion was optimal. Moreover, the accuracy of the training and prediction set was 95% and 92%, respectively. The method based on fusion features of image and spectral features can accurately and effectively diagnose the severity of FHB, thereby providing a technical basis for the timely and effective control of FHB and precise application of a pesticide.

Research on the Spot Color Matching System Based on Ink Volume

2010 ◽  
Vol 174 ◽  
pp. 64-67
Author(s):  
Wei Guo Bai ◽  
Xing Yue Hu ◽  
Yan Yan
Keyword(s):  
Visual Experience ◽  
Color Matching ◽  
Matching Model ◽  
Primary Color ◽  
Model Based ◽  
Spot Color

The admixture of Spot-color ink is generally determined by experience to make sure the type of primary color and the use of each ink, and then change the proportion of each primary color by the chromaticity control or visual experience. While this approach is difficult to admixture a qualified spot color ink, it also easily leads to the waste of ink. This paper did many researches and experiments on how to establish a spot color matching system based on the calculation of ink. Through the calculation of the largest ink thickness on paper, the model of the total estimated amount of printing based on image is established. Then this paper adopts the method of three stimulate value matching to establish the spot-color matching model based on Kubelka-Munk law. Through the combination of these two models above, spot color matching system based on ink calculation is finally established, and verified through the actual printing.

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