Hyperspectral image feature extraction method based on sparse constraint convolutional neural network

2017 ◽  
Author(s):  
Peiyuan Jia ◽  
Miao Zhang ◽  
Wenbo Yu ◽  
Yi Shen
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Extraction Method ◽  
Hyperspectral Image ◽  
Image Feature ◽  
Sparse Constraint ◽  
Image Feature Extraction ◽  
Feature Extraction Method

A modified welding image feature extraction algorithm for rotating arc narrow gap MAG welding

2015 ◽  
Vol 42 (3) ◽  
pp. 222-227 ◽  
Author(s):  
Wenhang Li ◽  
Yunhong Ji ◽  
Jing Wu ◽  
Jiayou Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Image Feature ◽  
Mag Welding ◽  
Narrow Gap ◽  
Image Feature Extraction ◽  
Content Type ◽  
Feature Extraction Method ◽  
Extraction Algorithm ◽  
Welding Image

Purpose The purpose of this paper is to provide a modified welding image feature extraction algorithm for rotating arc narrow gap metal active-gas welding (MAG) welding, which is significant for improving the accuracy and reliability of the welding process. Design/methodology/approach An infrared charge-coupled device (CCD) camera was utilized to obtain the welding image by passive vision. The left/right arc position was used as a triggering signal to capture the image when the arc is approaching left/right sidewall. Comparing with the conventional method, the authors’ sidewall detection method reduces the interference from arc; the median filter removes the welding spatter; and the size of the arc area was verified to reduce the reflection from welding pool. In addition, the frame loss was also considered in the authors’ method. Findings The modified welding image feature extraction method improves the accuracy and reliability of sidewall edge and arc position detection. Practical implications The algorithm can be applied to welding seam tracking and penetration control in rotating or swing arc narrow gap welding. Originality/value The modified welding image feature extraction method is robust to typical interference and, thus, can improve the accuracy and reliability of the detection of sidewall edge and arc position.

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