Position‐invariant two‐dimensional image correlation using a one‐dimensional space integrating optical processor: application to security verification

1996 ◽  
Vol 35 (9) ◽  
pp. 2479 ◽  
Author(s):  
Bahram Javidi
Keyword(s):  
Dimensional Space ◽  
Image Correlation ◽  
Two Dimensional ◽  
One Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image ◽  
Optical Processor ◽  
Security Verification

scholarly journals Determination of hard X-ray polarization from two-dimensional images

2020 ◽  
Vol 53 (6) ◽  
pp. 1559-1561
Author(s):  
Robert B. Von Dreele ◽  
Wenqian Xu
Keyword(s):  
Incident Beam ◽  
Beam Polarization ◽  
Two Dimensional ◽  
One Dimensional ◽  
X Ray ◽  
Dimensional Image ◽  
Two Dimensional Image ◽  
The Difference ◽  
Two Dimensional Images

An estimate of synchrotron hard X-ray incident beam polarization is obtained by partial two-dimensional image masking followed by integration. With the correct polarization applied to each pixel in the image, the resulting one-dimensional pattern shows no discontinuities arising from the application of the mask. Minimization of the difference between the sums of the masked and unmasked powder patterns allows estimation of the polarization to ±0.001.

Antigravity Slopes

2017 ◽  
Author(s):  
Kokichi Sugihara
Keyword(s):  
Human Brain ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Real Life ◽  
Two Dimensional ◽  
Single View ◽  
Dimensional Image ◽  
Two Dimensional Image ◽  
New Type ◽  
Law Of Gravity

A new type of illusion, called the antigravity slope illusion, is presented in this chapter. In this illusion a slope orientation is perceived opposite to the true orientation and hence a ball put on it appears to be rolling uphill, defying the law of gravity. This illusion is based on the ambiguity in the distance from a viewpoint to the surface of a three-dimensional solid represented in a single-view image. This illusion also arises in human real life, for example, when a car driver misunderstands the orientation of a road along which he or she is driving. Two assumptions are explored: (a) the human brain prefers to interpret vertical columns in a two-dimensional image as being vertical in three-dimensional space to being slanted and (b) the human brain prefers the most symmetric shape as the interpretation of a two-dimensional image.

scholarly journals Bayesian reconstruction ofP(r) directly from two-dimensional detector imagesviaa Markov chain Monte Carlo method

2013 ◽  
Vol 46 (2) ◽  
pp. 404-414 ◽  
Author(s):  
Sudeshna Paul ◽  
Alan M. Friedman ◽  
Chris Bailey-Kellogg ◽  
Bruce A. Craig
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Interatomic Distance ◽  
Scattering Data ◽  
Two Dimensional ◽  
One Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image

The interatomic distance distribution,P(r), is a valuable tool for evaluating the structure of a molecule in solution and represents the maximum structural information that can be derived from solution scattering data without further assumptions. Most current instrumentation for scattering experiments (typically CCD detectors) generates a finely pixelated two-dimensional image. In continuation of the standard practice with earlier one-dimensional detectors, these images are typically reduced to a one-dimensional profile of scattering intensities,I(q), by circular averaging of the two-dimensional image. Indirect Fourier transformation methods are then used to reconstructP(r) fromI(q). Substantial advantages in data analysis, however, could be achieved by directly estimating theP(r) curve from the two-dimensional images. This article describes a Bayesian framework, using a Markov chain Monte Carlo method, for estimating the parameters of the indirect transform, and thusP(r), directly from the two-dimensional images. Using simulated detector images, it is demonstrated that this method yieldsP(r) curves nearly identical to the referenceP(r). Furthermore, an approach for evaluating spatially correlated errors (such as those that arise from a detector point spread function) is evaluated. Accounting for these errors further improves the precision of theP(r) estimation. Experimental scattering data, where no ground truth referenceP(r) is available, are used to demonstrate that this method yields a scattering and detector model that more closely reflects the two-dimensional data, as judged by smaller residuals in cross-validation, thanP(r) obtained by indirect transformation of a one-dimensional profile. Finally, the method allows concurrent estimation of the beam center andDmax, the longest interatomic distance inP(r), as part of the Bayesian Markov chain Monte Carlo method, reducing experimental effort and providing a well defined protocol for these parameters while also allowing estimation of the covariance among all parameters. This method provides parameter estimates of greater precision from the experimental data. The observed improvement in precision for the traditionally problematicDmaxis particularly noticeable.

Instrumental and Experimental Considerations

2018 ◽  
Author(s):  
Eaton E. Lattman ◽  
Thomas D. Grant ◽  
Edward H. Snell
Keyword(s):  
Data Processing ◽  
Two Dimensional ◽  
Specific Data ◽  
One Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image ◽  
Data Processing Software ◽  
Processing Software

Thic chapter describes some of the processes that are often carried out within specific data processing software associated with an instrument but are invisible to the user. It is useful to be aware of them. These include dealing with detector artifacts and limitations, and the integration of the signal from a two-dimensional image to produce a one-dimensional scattering profile, among other steps.

scholarly journals A Self-Established “Machining-Measurement-Evaluation” Integrated Platform for Taper Cutting Experiments and Applications

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 929
Author(s):  
Xudong Yang ◽  
Zexiao Li ◽  
Linlin Zhu ◽  
Yuchu Dong ◽  
Lei Liu ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Precision Machining ◽  
Two Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image ◽  
Integrated Platform ◽  
Hard And Brittle Materials ◽  
Ultra Precision ◽  
Cutting Experiments

Taper-cutting experiments are important means of exploring the nano-cutting mechanisms of hard and brittle materials. Under current cutting conditions, the brittle-ductile transition depth (BDTD) of a material can be obtained through a taper-cutting experiment. However, taper-cutting experiments mostly rely on ultra-precision machining tools, which have a low efficiency and high cost, and it is thus difficult to realize in situ measurements. For taper-cut surfaces, three-dimensional microscopy and two-dimensional image calculation methods are generally used to obtain the BDTDs of materials, which have a great degree of subjectivity, leading to low accuracy. In this paper, an integrated system-processing platform is designed and established in order to realize the processing, measurement, and evaluation of taper-cutting experiments on hard and brittle materials. A spectral confocal sensor is introduced to assist in the assembly and adjustment of the workpiece. This system can directly perform taper-cutting experiments rather than using ultra-precision machining tools, and a small white light interference sensor is integrated for in situ measurement of the three-dimensional topography of the cutting surface. A method for the calculation of BDTD is proposed in order to accurately obtain the BDTDs of materials based on three-dimensional data that are supplemented by two-dimensional images. The results show that the cutting effects of the integrated platform on taper cutting have a strong agreement with the effects of ultra-precision machining tools, thus proving the stability and reliability of the integrated platform. The two-dimensional image measurement results show that the proposed measurement method is accurate and feasible. Finally, microstructure arrays were fabricated on the integrated platform as a typical case of a high-precision application.

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