Perfect vision [machine vision system]

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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A real-time machine vision system for visual information processing

Proceedings of IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.1994.400037 ◽

2002 ◽

Cited By ~ 3

Author(s):

L. Guan ◽

H. Kong

Keyword(s):

Information Processing ◽

Machine Vision ◽

Real Time ◽

Visual Information ◽

Vision System ◽

Visual Information Processing ◽

Machine Vision System ◽

Time Machine

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

Mechatronics ◽

10.1016/j.mechatronics.2006.01.001 ◽

2006 ◽

Vol 16 (5) ◽

pp. 243-247 ◽

Cited By ~ 15

Author(s):

Zhenwei Su ◽

Gui Yun Tian ◽

Chunhua Gao

Keyword(s):

Machine Vision ◽

Vision System ◽

Machine Vision System ◽

On Line ◽

Removal Of Contaminants

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A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.104728 ◽

2021 ◽

pp. 104728

Author(s):

Ahmad Jahanbakhshi ◽

Yousef Abbaspour-Gilandeh ◽

Kobra Heidarbeigi ◽

Mohammad Momeny

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Vision System ◽

Machine Vision System ◽

Novel Method

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Measuring radiata pine seedling morphological features using a machine vision system

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2021.106355 ◽

2021 ◽

Vol 189 ◽

pp. 106355

Author(s):

Benjamin McGuinness ◽

Mike Duke ◽

Chi Kit Au ◽

Shen Hin Lim

Keyword(s):

Machine Vision ◽

Vision System ◽

Radiata Pine ◽

Morphological Features ◽

Machine Vision System

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Research on Machine Vision in Forestry Pluck System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.139-141.2199 ◽

2010 ◽

Vol 139-141 ◽

pp. 2199-2202

Author(s):

Xin Li ◽

Chun Liang Zhang ◽

Li Jun Li ◽

Zhi Hu

Keyword(s):

Machine Vision ◽

Vision System ◽

Area Ratio ◽

Self Organization ◽

Machine Vision System ◽

Camellia Oleifera ◽

Threshold Method ◽

Input Feature ◽

Ga Algorithm ◽

Forestry Industry

Forestry industry is an important part of nation's economy. In this paper, a machine vision system is presented as a key module of Camellia oleifera pluck robot. In order to cut fruit image up from complicate background, SOFM neural network and gray thresh is used in image segmentation. In SOFM method, take R-B,G-R,G-B and hue H tunnel as input feature vectors, use self-organization network to clustering can get the best effect. in gray threshold method can take various of method to get the best threshold, such as PSO and GA algorithm, and MATLAB includes the toolboxes. At last use noise ratio, area ratio, divided time, Fourier boundary descriptors and other indicators to assess the accuracy of segmentation. The methods have the significance to the current and subsequent research of forestry pluck device.

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