A machine vision system for on-line removal of contaminants in wool

Mechatronics ◽  
2006 ◽  
Vol 16 (5) ◽  
pp. 243-247 ◽  
Author(s):  
Zhenwei Su ◽  
Gui Yun Tian ◽  
Chunhua Gao
2011 ◽  
Vol 291-294 ◽  
pp. 2624-2629 ◽  
Author(s):  
Qing Hua Wu ◽  
Na Dai ◽  
Tao He

A Circle-shape is an important figure in most small rule mechanical parts, and usually be measured to get the radius or used as a stand calibration mark. In this paper, a 2-D circle measurement system for small rule mechanical parts based on machine vision is designed and built. The basic components and work principle of the machine vision measurement system are introduced, and the measurement produce is designed and discussed. An available algorithm for circle contour detected and fitted is described. Using this algorithm, the measurement software flow and architecture are built and the software system realized in the Microsoft visual studio program platform. Certainly, the calibration of machine vision system is introduced also. Using the system and method introduced above, an experiment is designed to measure the outer ring radius of one certain model bearing. The measured data is processed and analyzed. Through the experiment and result, it can be found that the measurement system can get relatively high precision and the measurement method is relatively steady, and the system precision and speed can be suit for the demand of on-line and real-time circle measurement.


The present paper reports the development of a machine vision system for quality inspection of wheat using kernel shape attribute. Shape attribute of agricultural products including wheat kernels is extremely difficult to quantify in digital computation. A new method is proposed in the present work to quantify shape attribute of wheat kernels using regional boundary descriptors. Recognition task in the proposed machine vision system is carried out by neural classifier trained with Levenberg-Marquardt (LM) based supervised learning. Proposed neural classifier was executed using feed-forward backpropagation based three layer artificial neural network. Experimental results indicate more than 98.1% overall average classification accuracy for the involved wheat and impurity elements in the present work. The results of present study are quite promising and the proposed machine vision system has potential future for on-line inspection of agriculture produce in real time environment.


2010 ◽  
Vol 89 (6) ◽  
pp. 1252-1264 ◽  
Author(s):  
C.-C. Yang ◽  
K. Chao ◽  
M.S. Kim ◽  
D.E. Chan ◽  
H.L. Early ◽  
...  

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.


2005 ◽  
Vol 56 (8-9) ◽  
pp. 831-842 ◽  
Author(s):  
Monica Carfagni ◽  
Rocco Furferi ◽  
Lapo Governi

2012 ◽  
Vol 546-547 ◽  
pp. 1382-1386
Author(s):  
Yin Xia Liu ◽  
Ping Zhou

In order to promote the application and development of machine vision, The paper introduces the components of a machine vision system、common lighting technique and machine vision process. And the key technical problems are also briefly discussed in the application. A reference idea for application program of testing the quality of the machine parts is offered.


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