Overview of industrial machine vision

1984 ◽  
Vol 42 ◽  
pp. 550-553
Author(s):  
Jon R. Mandeville
Keyword(s):  
Machine Vision ◽  
Cognitive Processing ◽  
Vision System ◽  
Human Vision ◽  
Artificial Vision ◽  
Visual Environment ◽  
Computational System ◽  
Visual Sensing ◽  
Significant Research ◽  
Industrial Machine

One hardly needs to emphasize the importance of visual sensing and interpretation in our everyday lives and the severe limitations and handicaps imposed on the blind. The importance of human vision has motivated an intense interest in and significant research on artificial vision systems over the last 20 years. The relatively new, fast growing discipline of “machine (or computer) vision” is an outgrowth of this research.Machine vision is computation on images to produce useful, meaningful descriptions of a visual environment. A machine vision system may be used either as a symbiotic enhancement to human vision or as the perceptual component of an autonomous, goal-driven computational system capable of cognitive processing.

Symbolic Pointillism: Computer Art Motivated by Human Brain Structures

Leonardo ◽  
2005 ◽  
Vol 38 (4) ◽  
pp. 337-340 ◽  
Author(s):  
Norbert Krüger ◽  
Florentin Wörgötter
Keyword(s):  
Visual System ◽  
Human Brain ◽  
Human Visual System ◽  
Vision System ◽  
Visual Representations ◽  
Brain Structures ◽  
Human Vision ◽  
Artificial Vision ◽  
Computer Art ◽  
Functional Aspects

The authors introduce a new kind of computer art motivated by cortical structures in the human visual system. This type of computer art is related to the sub-group of the impressionist art movement called pointillism. However, while pointillism visualizes and makes use of processes that have been associated with the human eye, Symbolic Pointillism also makes cortical processes explicit. The visual representations underlying this art have been developed during a project that aims at the transfer of functional aspects of human vision to artificial systems. The authors have applied their findings in such an artificial vision system and in a sound/vision installation.

Robust Machine Vision-Based Parts Inspection: Intelligent Neuro-Fuzzy Versus Threshold Based Classification

2007 ◽  
Author(s):  
Jonathan Killing ◽  
Brian W. Surgenor ◽  
Chris K. Mechefske
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Inspection System ◽  
Operating Environment ◽  
Image Processing Algorithm ◽  
Intelligent Algorithm ◽  
Analysis Algorithm ◽  
Neuro Fuzzy ◽  
Robust System ◽  
Industrial Machine

Past experience with an industrial machine vision-based parts inspection system highlighted the need for a robust system, that is a vision system that could adapt to changes in the operating environment without requiring excessive retuning of the data analysis algorithm. With this need in mind, an intelligent neuro-fuzzy based image processing algorithm was developed and tested against a traditional threshold based algorithm. Experimental results indicate that the intelligent algorithm performs well when the data is not well segmented.

New Stereovision for Human-Robot Communications

2000 ◽  
Vol 12 (3) ◽  
pp. 231-234
Author(s):  
Junichi Takeno ◽  
◽  
Zichuan Xu
Keyword(s):  
Vision System ◽  
Artificial Vision ◽  
Human Behaviors ◽  
Sensing System ◽  
Visual Sensing ◽  
Five Senses ◽  
Binocular Stereovision ◽  
Human Sense ◽  
New System ◽  
Blind Spots

Better communications between robots and humans requires an understanding of human senses, and at the same time, the ability to express these senses. Robot functions must be able to approximate as much as possible the five human senses of sight, hearing, smell, taste and touch. Further, the system in which these five senses are integrated must be understood. The expression of such senses by robots should not be confined to merely simulating human behaviors but needs to address unknown and difficult problems related to the manifestation of the inner aspects of human mentality. In this paper, we report on a new artificial vision system that is similar to the human sense of sight, with the target of realizing functions for understanding the processes of human sight. First, we introduce the new artificial visual sensing system and explain problems of virtual images and occlusion, blind spots in conventional binocular stereovision systems. We then discuss a technique for solving these problems that utilizes the new functions of the artificial vision system we developed and introduce a prototype of the new system and its configuration.

Computational Model for Machine Vision Inspection Based on Vision Attention

2011 ◽  
Vol 383-390 ◽  
pp. 2398-2403
Author(s):  
Jin Fang Shi ◽  
Zhen Wei Su ◽  
Guo Hui Li
Keyword(s):  
Machine Vision ◽  
Computational Model ◽  
Vision System ◽  
Visual Selective Attention ◽  
Human Vision ◽  
Human Vision System ◽  
Top Down ◽  
Bottom Up ◽  
Vision Inspection ◽  
Machine Vision Inspection

Human vision system exploits this fact by visual selective attention mechanisms towards important and informative regions. A computational model of combination both bottom-up and top-down simulating human vision system for machine vision inspection is proposed. In this model, top-down knowledge-based information is highlighted to integrate into bottom-up stimulus-based process of vision attention. The model is tested on inspecting contaminants in cotton images. Experiment result shows that the proposed model is feasible and effective in visual inspection. And it is available and quasi-equivalent to human vision attention.

Multifunctional stereoscopic machine vision system with multiple 3D outputs

2020 ◽  
Keyword(s):  
Machine Vision ◽  
International Symposium ◽  
Fast Track ◽  
Vision System ◽  
Machine Vision System ◽  
Stereoscopic Displays ◽  
Electronic Imaging

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

scholarly journals A real-time machine-vision system for monitoring the textile raising process

2005 ◽  
Vol 56 (8-9) ◽  
pp. 831-842 ◽  
Author(s):  
Monica Carfagni ◽  
Rocco Furferi ◽  
Lapo Governi
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Vision System ◽  
Machine Vision System ◽  
Time Machine

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

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