Image feature extraction method of planar target based on homography

2011 ◽  
Author(s):  
Bin Wu ◽  
Xin-tong Xiao ◽  
Ting Xue ◽  
Xue-you Yang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Feature Extraction Method ◽  
Planar Target

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.

Wavelet Denoising-Based Image Feature Extraction by Independent Component Analysis without Pre-Whitening

2013 ◽  
Vol 380-384 ◽  
pp. 3830-3833
Author(s):  
Xing Jia Tang ◽  
Xiu Fang Zhang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Wavelet Transformation ◽  
Simulation Experiment ◽  
Wavelet Denoising ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Feature Extraction Method ◽  
Residual Noise ◽  
Fastica Algorithm

This paper proposes an ICA image feature extraction method without pre-whitening based on wavelet denoising. Firstly, we conduct denoising for noisy observed images with wavelet transformation; then the feature extraction for denoised observed images is done by using FastICA algorithm without pre-whitening; finally, we further remove the residual noise in extracted feature images with wavelet transformation. Simulation experiment results show that this method is apparent in the performance of denoising, meanwhile, the extracted feature images can distinguish the texture and shape feature well, which has stronger practicability and validity.

The Image Feature Extraction Algorithm Based on the DWT and the Improved 2DPCA

2014 ◽  
Vol 556-562 ◽  
pp. 5042-5045 ◽  
Author(s):  
Wu Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Target Recognition ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Two Dimension ◽  
Feature Extraction Method ◽  
Extraction Algorithm ◽  
New Feature ◽  
Pca Algorithm

The technology of 2DPCA is the feature extraction method proposed aiming at two-dimension image based on the traditional PCA algorithm. The paper proposed a improved weighting 2DPCA algorithm, combined with the two-dimension discrete DWT to handle the image, posing the new feature abstraction method, experiment improved that the new feature abstraction method can improve the target recognition efficiently compared with the original 2DPCA algorithm.

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