Machine Vision Systems

1988 ◽  
pp. 233-263
Author(s):  
V. Daniel Hunt
Keyword(s):  
Machine Vision ◽  
Vision Systems

scholarly journals Machine Vision Systems for Plant Growth Measurement and Modeling

2006 ◽  
Vol 44 (3) ◽  
pp. 181-187 ◽  
Author(s):  
Ta-Te LIN ◽  
Chung-Fang CHIEN ◽  
Wen-Chi LIAO ◽  
Kuo-Chi CHUNG ◽  
Jen-Min CHANG
Keyword(s):  
Plant Growth ◽  
Machine Vision ◽  
Vision Systems ◽  
Growth Measurement ◽  
Plant Growth Measurement

Real-time coherent optical correlator for machine vision systems

1992 ◽  
Vol 31 (4) ◽  
pp. 789 ◽  
Author(s):  
Victor V. Nekrasov
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Vision Systems ◽  
Optical Correlator

scholarly journals Fusing Data Processing in the Construction of Machine Vision Systems in Robotic Complexes

2019 ◽  
Vol 224 ◽  
pp. 04009 ◽  
Author(s):  
Aleksandr Zelensky ◽  
Evgenii Semenishchev ◽  
Aleksandr Gavlicky ◽  
Irina Tolstova ◽  
V. Frantc
Keyword(s):  
Machine Vision ◽  
Electromagnetic Interference ◽  
Visual Information ◽  
Vision Systems ◽  
Noise Component ◽  
New Approach ◽  
Common Causes ◽  
Robotic Cells ◽  
Combined Images

The development of machine vision systems is based on the analysis of visual information recorded by sensitive matrices. This information is most often distorted by the presence of interfering factors represented by a noise component. The common causes of the noise include imperfect sensors, dust and aerosols, used ADCs, electromagnetic interference, and others. The presence of these noise components reduces the quality of the subsequent analysis. To implement systems that allow operating in the presence of a noise, a new approach, which allows parallel processing of data obtained in various electromagnetic ranges, has been proposed. The primary area of application of the approach are machine vision systems used in complex robotic cells. The use of additional data obtained by a group of sensors allows the formation of arrays of usefull information that provide successfull optimization of operations. The set of test data shows the applicability of the proposed approach to combined images in machine vision systems.

Deep Learning: The Good, the Bad, and the Ugly

2019 ◽  
Vol 5 (1) ◽  
pp. 399-426 ◽  
Author(s):  
Thomas Serre
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Vision ◽  
Artificial Vision ◽  
Vision Systems ◽  
Comprehensive Overview ◽  
Biological Vision ◽  
Recent Developments ◽  
Visual Intelligence ◽  
Modern Machine

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

Machine Vision Systems

2011 ◽  
pp. 1-3
Author(s):  
Andreas Wedel ◽  
Daniel Cremers
Keyword(s):  
Machine Vision ◽  
Vision Systems

Design of Industrial Machine Vision Systems

1995 ◽  
pp. 1-24
Author(s):  
G. J. Awcock ◽  
R. Thomas
Keyword(s):  
Machine Vision ◽  
Vision Systems ◽  
Industrial Machine
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