Measurement Errors in Polarization Vision Systems

1996 ◽  
Author(s):  
B. Liang ◽  
A.M. Wallace ◽  
E. Trucco
Keyword(s):  
Measurement Errors ◽  
Polarization Vision ◽  
Vision Systems

Precision Positioning With Force Sensing and Fuzzy Control

1991 ◽  
Author(s):  
H. Gürocak ◽  
A. de Sam Lazaro
Keyword(s):  
Fuzzy Control ◽  
Measurement Errors ◽  
Use Of Force ◽  
Force Sensing ◽  
Precision Positioning ◽  
Vision Systems ◽  
System Noise ◽  
Manipulator Control ◽  
Established Technique

Abstract The use of force feed back for manipulator control is a well established technique. However, measurement inaccuracies and system noise precluded any practical deployment of this method. In this paper an approach to force sensing and measurement will be discussed. A method of accounting for measurement errors and system noise will be introduced. The use of fuzzy control for fine positioning will be presented. The method described in this paper provides an inexpensive alternative to vision systems using a software approach.

Measurement errors in polarization-based 3D vision systems

1997 ◽  
Author(s):  
Bojian Liang ◽  
Andrew M. Wallace ◽  
Emanuele Trucco
Keyword(s):  
Measurement Errors ◽  
Vision Systems ◽  
3D Vision

Digital Projector Calibration for 3-D Active Vision Systems

2000 ◽  
Vol 124 (1) ◽  
pp. 126-134 ◽  
Author(s):  
Tzung-Sz Shen ◽  
Chia-Hsiang Menq
Keyword(s):  
Rigid Body ◽  
Measurement Errors ◽  
Structured Light ◽  
Active Vision ◽  
Calibration Method ◽  
Lens Distortion ◽  
Vision Systems ◽  
Projector Calibration ◽  
Rigid Body Transformation ◽  
Light Beams

3-D active vision systems that project artificial structured light for coordinate measurements have been adopted in many industrial applications. With advances of electronic projection display technology, the digital projector is becoming an important component of various 3-D active vision systems. However, current projector models or structured light calibration techniques for 3-D active vision systems are limited to stripe-type structured light and the majority of them do not consider projector lens distortion. In order to overcome these limitations, a digital projector calibration method is developed to calibrate light beams projected from all pixel elements of a digital projector. Since the digital projector is fully programmable, various structured light patterns can be projected for coordinate acquisition, whose models can be obtained by interpolating parameters of light beams that synthesize the structured light patterns. With proper interpolation functions, experimental results indicate that the projector lens distortion can be successfully compensated and measurement errors are significantly reduced. When the digital projector is moved, a simple rigid body transformation calibration method is developed to rapidly obtain the transformation without re-calibrating the projector. The precision of the 3-D active vision system using the proposed digital projector calibration method and rigid body transformation calibration technique is experimentally evaluated.

scholarly journals Polarization distance: a framework for modelling object detection by polarization vision systems

2014 ◽  
Vol 281 (1776) ◽  
pp. 20131632 ◽  
Author(s):  
Martin J. How ◽  
N. Justin Marshall
Keyword(s):  
Object Detection ◽  
Polarized Light ◽  
Natural World ◽  
Large Field ◽  
Polarization Vision ◽  
Vision Systems ◽  
Natural Conditions ◽  
Vertical Arrays ◽  
Object Based ◽  
Receptor Dynamics

The discrimination of polarized light is widespread in the natural world. Its use for specific, large-field tasks, such as navigation and the detection of water bodies, has been well documented. Some species of cephalopod and crustacean have polarization receptors distributed across the whole visual field and are thought to use polarized light cues for object detection. Both object-based polarization vision systems and large field detectors rely, at least initially, on an orthogonal, two-channel receptor organization. This may increase to three-directional analysis at subsequent interneuronal levels. In object-based and some of the large-field tasks, the dominant e-vector detection axes are often aligned (through eye, head and body stabilization mechanisms) horizontally and vertically relative to the outside world. We develop Bernard and Wehner's 1977 model of polarization receptor dynamics to apply it to the detection and discrimination of polarized objects against differently polarized backgrounds. We propose a measure of ‘polarization distance’ (roughly analogous to ‘colour distance’) for estimating the discriminability of objects in polarized light, and conclude that horizontal/vertical arrays are optimally designed for detecting differences in the degree, and not the e-vector axis, of polarized light under natural conditions.

Precise on-line Measurement of Lattice Vectors

1990 ◽  
Vol 48 (1) ◽  
pp. 530-531
Author(s):  
W.J. de Ruijter ◽  
Sharma Renu
Keyword(s):  
Electron Microscope ◽  
Measurement Errors ◽  
Dynamic Range ◽  
Structural Information ◽  
Statistical Parameter ◽  
Systematic Errors ◽  
Geometric Distortion ◽  
Crystal Silicon ◽  
Camera System ◽  
On Line

Established methods for measurement of lattice spacings and angles of crystalline materials include x-ray diffraction, microdiffraction and HREM imaging. Structural information from HREM images is normally obtained off-line with the traveling table microscope or by the optical diffractogram technique. We present a new method for precise measurement of lattice vectors from HREM images using an on-line computer connected to the electron microscope. It has already been established that an image of crystalline material can be represented by a finite number of sinusoids. The amplitude and the phase of these sinusoids are affected by the microscope transfer characteristics, which are strongly influenced by the settings of defocus, astigmatism and beam alignment. However, the frequency of each sinusoid is solely a function of overall magnification and periodicities present in the specimen. After proper calibration of the overall magnification, lattice vectors can be measured unambiguously from HREM images.Measurement of lattice vectors is a statistical parameter estimation problem which is similar to amplitude, phase and frequency estimation of sinusoids in 1-dimensional signals as encountered, for example, in radar, sonar and telecommunications. It is important to properly model the observations, the systematic errors and the non-systematic errors. The observations are modelled as a sum of (2-dimensional) sinusoids. In the present study the components of the frequency vector of the sinusoids are the only parameters of interest. Non-systematic errors in recorded electron images are described as white Gaussian noise. The most important systematic error is geometric distortion. Lattice vectors are measured using a two step procedure. First a coarse search is obtained using a Fast Fourier Transform on an image section of interest. Prior to Fourier transformation the image section is multiplied with a window, which gradually falls off to zero at the edges. The user indicates interactively the periodicities of interest by selecting spots in the digital diffractogram. A fine search for each selected frequency is implemented using a bilinear interpolation, which is dependent on the window function. It is possible to refine the estimation even further using a non-linear least squares estimation. The first two steps provide the proper starting values for the numerical minimization (e.g. Gauss-Newton). This third step increases the precision with 30% to the highest theoretically attainable (Cramer and Rao Lower Bound). In the present studies we use a Gatan 622 TV camera attached to the JEM 4000EX electron microscope. Image analysis is implemented on a Micro VAX II computer equipped with a powerful array processor and real time image processing hardware. The typical precision, as defined by the standard deviation of the distribution of measurement errors, is found to be <0.003Å measured on single crystal silicon and <0.02Å measured on small (10-30Å) specimen areas. These values are ×10 times larger than predicted by theory. Furthermore, the measured precision is observed to be independent on signal-to-noise ratio (determined by the number of averaged TV frames). Obviously, the precision is restricted by geometric distortion mainly caused by the TV camera. For this reason, we are replacing the Gatan 622 TV camera with a modern high-grade CCD-based camera system. Such a system not only has negligible geometric distortion, but also high dynamic range (>10,000) and high resolution (1024x1024 pixels). The geometric distortion of the projector lenses can be measured, and corrected through re-sampling of the digitized image.

Development of reference materials with the composition of propyl and isopropyl alcohol solutions

2020 ◽  
pp. 66-72
Author(s):  
Irina A. Piterskikh ◽  
Svetlana V. Vikhrova ◽  
Nina G. Kovaleva ◽  
Tatyana O. Barynskaya
Keyword(s):  
Reference Materials ◽  
Measurement Errors ◽  
Isopropyl Alcohol ◽  
Certified Reference Materials ◽  
Toxic Substances ◽  
Biological Objects ◽  
Margin Of Error ◽  
Measurement Results ◽  
And Control ◽  
Characteristic Mass

Certified reference materials (CRM) composed of propyl (11383-2019) and isopropyl (11384-2019) alcohols solutions were created for validation of measurement procedures and control of measurement errors of measurement results of mass concentrations of toxic substances (alcohol) in biological objects (urine, blood) and water. Two ways of establishing the value of the certified characteristic – mass consentration of propanol-1 or propanol-2 have been studied. The results obtained by the preparation procedure and comparison with the standard are the same within the margin of error.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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