Two-Dimensional Image Processing By One-Dimensional Filtering Of Projection Data

1983 ◽  
Author(s):  
A. F. Gmitro ◽  
G. R. Gindi ◽  
H. H. Barrett ◽  
R. L. Easton
Keyword(s):  
Image Processing ◽  
Projection Data ◽  
Two Dimensional ◽  
One Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image

scholarly journals Flexible and High Performance ASIPs for Pixel Level Image Processing and Two Dimensional Image Processing

2013 ◽  
Vol 21 (3) ◽  
pp. 552-562
Author(s):  
Hsuan-Chun Liao ◽  
Mochamad Asri ◽  
Tsuyoshi Isshiki ◽  
Dongju Li ◽  
Hiroaki Kunieda
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Two Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image

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.

The VLSI design of a two dimensional image processing array

1984 ◽  
Vol 14 (3-4) ◽  
pp. 125-132 ◽  
Author(s):  
Dimokritos Panogiotopoulos ◽  
Nikolaos Bourbakis
Keyword(s):  
Image Processing ◽  
Vlsi Design ◽  
Two Dimensional ◽  
Dimensional Image ◽  
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.

Application of the ICL DAP for two-dimensional image processing

1982 ◽  
Vol 26 (3-4) ◽  
pp. 455-457 ◽  
Author(s):  
N.R. Arnot ◽  
G.G. Wilkinson ◽  
R.E. Burge
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image
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