A method for discrimination of processed ginger based on image color feature and a support vector machine model

2016 ◽  
Vol 8 (10) ◽  
pp. 2201-2206 ◽  
Author(s):  
Sujuan Zhou ◽  
Jiang Meng ◽  
Zhanpeng Huang ◽  
Shizhong Jiang ◽  
Yongqiu Tu
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Machine Model ◽  
Color Feature ◽  
Processed Products

The image color feature was analysed using a support vector machine and principal component analysis to discriminate different processed products of ginger.

Quality Diagnosis of Joints in Laser Brazing Based on Principal Component Analysis-Support Vector Machine Model

2017 ◽  
Vol 44 (3) ◽  
pp. 0302004
Author(s):  
程力勇 Cheng Liyong ◽  
米高阳 Mi Gaoyang ◽  
黎 硕 Li Shuo ◽  
胡席远 Hu Xiyuan ◽  
王春明 Wang Chunming
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Laser Brazing ◽  
Machine Model ◽  
Quality Diagnosis

scholarly journals Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1531
Author(s):  
Shanshan Huang ◽  
Yikun Yang ◽  
Xin Jin ◽  
Ya Zhang ◽  
Qian Jiang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Image Fusion ◽  
Principal Component ◽  
Sliding Window ◽  
Component Analysis ◽  
Image Features ◽  
Support Vector ◽  
Source Image ◽  
Machine Model

Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of the prominent advantages. In this work, a new multi-sensor image fusion method based on the support vector machine and principal component analysis is proposed. First, the key features of the source images are extracted by combining the sliding window technique and five effective evaluation indicators. Second, a trained support vector machine model is used to extract the focus region and the non-focus region of the source images according to the extracted image features, the fusion decision is therefore obtained for each source image. Then, the consistency verification operation is used to absorb a single singular point in the decisions of the trained classifier. Finally, a novel method based on principal component analysis and the multi-scale sliding window is proposed to handle the disputed areas in the fusion decision pair. Experiments are performed to verify the performance of the new combined method.

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