Camera Systems in Machine Vision

Recent developments in machine vision systems, solid state cameras, and image processing are reviewed. Both hardware and software systems are currently available for performing real-time recognition and geometric measurements. More than 1000 units of these imaging systems are already being used in manufacturing plants in the United States. Current research efforts are focused on the processing of three-dimensional information and on knowledge-based processing systems. Five potential research topics in the area of photogrammetry are proposed: 1) stereo solid state camera systems, 2) image correlation, 3) self-calibration and self-orientation, 4) general algorithm for multistation and multicamera photography, and 5) artificial photogrammetry.

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Extended noise equalisation for image compression in microscopical applications

tm - Technisches Messen ◽

10.1515/teme-2019-0022 ◽

2019 ◽

Vol 86 (7-8) ◽

pp. 422-432

Author(s):

Daniel M. Kirchhöfer ◽

Gerhard A. Holst ◽

Fred S. Wouters ◽

Stephan Hock ◽

Bernd Jähne

Keyword(s):

Machine Vision ◽

Image Compression ◽

Image Sensor ◽

Image Noise ◽

Noise Variance ◽

Linear Amplification ◽

Temporal Noise ◽

Camera Systems ◽

Illumination System ◽

Photo Response

AbstractToday’s camera systems used for machine vision and scientific applications have intra-scene dynamic ranges up to 16 bit and therefore A/D converters with up to 16 bit resolution per pixel. Unfortunately, the linear amplification of electrons also forces a linear or even quadratic increase of the image noise variance with the signal. Based on a method published in 2016 (B. Jähne, M. Schwarzbauer, tm-Technisches Messen 83.1), this paper describes a more general nonlinear transformation which equalizes the combined effect of temporal noise and photo-response non-uniformity (PRNU) and/or temporal noise in the illumination system of an image sensor. With this generalisation it is possible to use the equalisation also for microscopic applications for which an example is discussed.

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Camera Systems in Machine Vision

Handbook of Machine and Computer Vision ◽

10.1002/9783527413409.ch6 ◽

2017 ◽

pp. 317-397

Author(s):

Horst Mattfeldt

Keyword(s):

Machine Vision ◽

Camera Systems

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Designing high-resolution slow scan CCD-based TEM camera systems

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010012936x ◽

1992 ◽

Vol 50 (2) ◽

pp. 946-947

Author(s):

James F. Mancuso ◽

William B. Maxwell ◽

Russell E. Camp ◽

Mark H. Ellisman

Keyword(s):

Fiber Optics ◽

Ccd Camera ◽

High Energy ◽

Phosphor Layer ◽

X Ray ◽

Light Production ◽

Camera Systems ◽

X Ray Imaging ◽

Electron Image ◽

Scintillating Fiber

The imaging requirements for 1000 line CCD camera systems include resolution, sensitivity, and field of view. In electronic camera systems these characteristics are determined primarily by the performance of the electro-optic interface. This component converts the electron image into a light image which is ultimately received by a camera sensor.Light production in the interface occurs when high energy electrons strike a phosphor or scintillator. Resolution is limited by electron scattering and absorption. For a constant resolution, more energy deposition occurs in denser phosphors (Figure 1). In this respect, high density x-ray phosphors such as Gd2O2S are better than ZnS based cathode ray tube phosphors. Scintillating fiber optics can be used instead of a discrete phosphor layer. The resolution of scintillating fiber optics that are used in x-ray imaging exceed 20 1p/mm and can be made very large. An example of a digital TEM image using a scintillating fiber optic plate is shown in Figure 2.

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Machine Vision

10.1017/cbo9781139168229 ◽

2009 ◽

Cited By ~ 15

Author(s):

Wesley E. Snyder ◽

Hairong Qi

Keyword(s):

Machine Vision

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Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

Light & Engineering ◽

10.33383/2018-135 ◽

2018 ◽

pp. 143-149 ◽

Cited By ~ 1

Author(s):

Ruijie CHENG

Keyword(s):

Control System ◽

Machine Vision ◽

Energy Saving ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Lighting Control ◽

Lighting Energy ◽

Neural Network Algorithm ◽

Energy Saving Control ◽

Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multifeedback model for energysaving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

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