scholarly journals Physically-Accurate Synthetic Images for Machine Vision Design

1999 ◽  
Vol 121 (4) ◽  
pp. 763-770 ◽  
Author(s):  
J. M. Parker ◽  
Kok-Meng Lee
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Finite Size ◽  
Image Synthesis ◽  
Design Parameters ◽  
Synthesis Methods ◽  
Ambient Lighting ◽  
Wide Range ◽  
Synthetic Images ◽  
Synthesis Methodology

In machine vision applications, accuracy of the image far outweighs image appearance. This paper presents physically-accurate image synthesis as a flexible, practical tool for examining a large number of hardware/software configuration combinations for a wide range of parts. Synthetic images can efficiently be used to study the effects of vision system design parameters on image accuracy, providing insight into the accuracy and efficiency of image-processing algorithms in determining part location and orientation for specific applications, as well as reducing the number of hardware prototype configurations to be built and evaluated. We present results illustrating that physically accurate, rather than photo-realistic, synthesis methods are necessary to sufficiently simulate captured image gray-scale values. The usefulness of physically-accurate synthetic images in evaluating the effect of conditions in the manufacturing environment on captured images is also investigated. The prevalent factors investigated in this study are the effects of illumination, the sensor non-linearity and the finite-size pinhole on the captured image of retroreflective vision sensing and, therefore, on camera calibration was shown; if not fully understood, these effects can introduce apparent error in calibration results. While synthetic images cannot fully compensate for the real environment, they can be efficiently used to study the effects of ambient lighting and other important parameters, such as true part and environment reflectance, on image accuracy. We conclude with an evaluation of results and recommendations for improving the accuracy of the synthesis methodology.

Image synthesis methodology for algorithm testing and vision system design

2002 ◽  
Vol 216 (5) ◽  
pp. 669-682 ◽  
Author(s):  
J M Parker ◽  
K-M Lee
Keyword(s):  
Image Processing ◽  
System Design ◽  
Vision System ◽  
Image Synthesis ◽  
Image Understanding ◽  
Design Parameters ◽  
Wide Range ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Synthesis Methodology

Although it is well-recognized and widely accepted that vision adds considerable flexibility, and it has also been shown that numerical simulation can aid in image understanding and vision system design (significantly reducing the engineering time to design and implement such systems), the utilization of image synthesis as an aid in algorithm and system design still remains a largely underexplored area. In machine vision applications, accuracy of the image generally outweighs image appearance. Unfortunately, the focus of most commercially available simulation methods is on photorealistic image synthesis; this is insufficient to design vision systems or evaluate and compare image-processing algorithms for part-presentation tasks: physically accurate, rather than photo-realistic, synthesis methods are necessary to sufficiently simulate captured image grey-scale values. This paper presents a methodology to generate physically accurate synthetic images efficiently in order to provide an accurate, flexible and practical means of evaluating the performance of image-processing algorithms for numerous hardware/software configuration combinations and a wide range of parts. While the synthesis methodology cannot fully compensate for the real environment, it can be used efficiently to study the effects of vision system design parameters on image accuracy. This provides an insight into the efficacy of the design and the ability of suggested image-processing algorithms to perform adequately for specific applications; furthermore, it may provide a means for correcting apparent errors in image-processing results.

Applying Optoelectronic Devices Fusion in Machine Vision

2020 ◽  
pp. 184-213
Author(s):  
Wendy Flores-Fuentes ◽  
Moises Rivas-Lopez ◽  
Daniel Hernandez-Balbuena ◽  
Oleg Sergiyenko ◽  
Julio C. Rodríguez-Quiñonez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Vision ◽  
Robot Navigation ◽  
Vision System ◽  
Optoelectronic Devices ◽  
Structural Monitoring ◽  
Machine Vision System ◽  
Wide Range ◽  
Spatial Coordinates ◽  
Electronic Hardware

Machine vision is supported and enhanced by optoelectronic devices, the output from a machine vision system is information about the content of the optoelectronic signal, it is the process whereby a machine, usually a digital computer and/or electronic hardware automatically processes an optoelectronic signal and reports what it means. Machine vision methods to provide spatial coordinates measurement has developed in a wide range of technologies for multiples fields of applications such as robot navigation, medical scanning, and structural monitoring. Each technology with specified properties that could be categorized as advantage and disadvantage according its utility to the application purpose. This chapter presents the application of optoelectronic devices fusion as the base for those systems with non-lineal behavior supported by artificial intelligence techniques, which require the use of information from various sensors for pattern recognition to produce an enhanced output.

Applying Optoelectronic Devices Fusion in Machine Vision

2017 ◽  
pp. 1-37
Author(s):  
Wendy Flores-Fuentes ◽  
Moises Rivas-Lopez ◽  
Daniel Hernandez-Balbuena ◽  
Oleg Sergiyenko ◽  
Julio C. Rodríguez-Quiñonez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Vision ◽  
Robot Navigation ◽  
Vision System ◽  
Optoelectronic Devices ◽  
Structural Monitoring ◽  
Machine Vision System ◽  
Wide Range ◽  
Spatial Coordinates ◽  
Electronic Hardware

Machine vision is supported and enhanced by optoelectronic devices, the output from a machine vision system is information about the content of the optoelectronic signal, it is the process whereby a machine, usually a digital computer and/or electronic hardware automatically processes an optoelectronic signal and reports what it means. Machine vision methods to provide spatial coordinates measurement has developed in a wide range of technologies for multiples fields of applications such as robot navigation, medical scanning, and structural monitoring. Each technology with specified properties that could be categorized as advantage and disadvantage according its utility to the application purpose. This chapter presents the application of optoelectronic devices fusion as the base for those systems with non-lineal behavior supported by artificial intelligence techniques, which require the use of information from various sensors for pattern recognition to produce an enhanced output.

Gray Image Composition Method for Measurement Based on Multi-Software Cooperation

2014 ◽  
Vol 889-890 ◽  
pp. 1052-1056
Author(s):  
Fei Hao ◽  
Song Qing Zhu ◽  
Hai Tao Gao
Keyword(s):  
Machine Vision ◽  
Three Dimensional ◽  
Image Synthesis ◽  
Image Composition ◽  
Gray Image ◽  
Engineering Software ◽  
Three Dimensional Modeling ◽  
Dimensional Modeling ◽  
Modeling Software ◽  
Synthetic Images

One method named image synthesis was proposed for gray image composition to meet the requirement that algorithm research of machine vision needed a lot of images. Firstly, two modules, i.e., modeling module and camera module, of some engineering software were compared and images will be obtained by using three-dimensional modeling software cooperating with VRay plug-in. Then, parameter setting and adjustment for VRay plug-in was studied. Lastly, measurement experiments were carried out by using synthetic images. The experimental results demonstrate that 1μm measuring accuracy is possible under the condition that the distance between calibration part and camera was equal to that between parts and camera. The synthetic images can be used for algorithm research of machine vision.

scholarly journals Bilgisayarlı Görüntüleme Sistemi: Gıda Endüstrisinde Kullanımı ve Uygulamaları

2019 ◽  
Vol 7 (7) ◽  
pp. 989
Author(s):  
Pinar Balkir ◽  
Kemal Kemahlıoğlu ◽  
Ufuk Yücel
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Fruits And Vegetables ◽  
Vision System ◽  
Computer Hardware ◽  
Machine Vision System ◽  
Food Sector ◽  
Image Capture ◽  
Wide Range ◽  
Marine Products

Machine vision system is a combination of camera, image capture card, computer hardware and image processing technology. Safe foods are highly preferred by consumers today and accordingly, machine vision system has the edge on food sector for ensuring qualitative data and accelerating some certain processes. Machine vision system, which is more accurate, reliable and faster than conventional methods, has been used in wide range of applications in the inspection of cereals, fruits and vegetables, meats and marine products and some other processed foods in combination with convenient image processing and analysing algorithms. Considering the objectivity, promptness, economy and effectiveness as the chief advantages, the system makes progress as an alternative method in the sector.

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.

Application of Machine Vision System in the Precision Inspection

2012 ◽  
Vol 546-547 ◽  
pp. 1382-1386
Author(s):  
Yin Xia Liu ◽  
Ping Zhou
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Technical Problems ◽  
Application Program ◽  
Precision Inspection ◽  
Machine Parts

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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