P1-124: VALIDATION OF WEIGHTED TWO-POINT CORRELATION ANALYSIS ON TAU PET WITHOUT IMAGE INTENSITY NORMALIZATION

2006 ◽  
Vol 14 (7S_Part_5) ◽  
pp. P318-P318
Author(s):  
Sepideh Shokouhi ◽  
Hakmook Kang ◽  
Harry E. Gwirtsman ◽  
Paul A. Newhouse
Keyword(s):  
Correlation Analysis ◽  
Image Intensity ◽  
Point Correlation ◽  
Tau Pet ◽  
Intensity Normalization

scholarly journals Quantitative Computerized Two-Point Correlation Analysis of Lung CT Scans Correlates With Pulmonary Function in Pulmonary Sarcoidosis

CHEST Journal ◽  
2012 ◽  
Vol 142 (6) ◽  
pp. 1589-1597 ◽  
Author(s):  
Barbaros Selnur Erdal ◽  
Elliott D. Crouser ◽  
Vedat Yildiz ◽  
Mark A. King ◽  
Andrew T. Patterson ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Pulmonary Function ◽  
Pulmonary Sarcoidosis ◽  
Ct Scans ◽  
Point Correlation ◽  
Lung Ct

scholarly journals The Forest of Absorption Lines in QSO Spectra - A Key to the Large Scale Structure Development?

1987 ◽  
Vol 121 ◽  
pp. 337-339
Author(s):  
J. P. Mücket ◽  
V. Müller
Keyword(s):  
High Resolution ◽  
Correlation Analysis ◽  
Correlation Functions ◽  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Absorption Lines ◽  
Structure Development ◽  
Point Correlation ◽  
Quasiperiodic Structure

For six published high resolution QSO spectra a correlation analysis of unidentified absorption lines is performed. The two-point correlation functions typically show some quasiperiodic structure. The results allow for the interpretation that absorbing clouds lie in sheetlike structures as predicted by the pancake theory.

scholarly journals Cataloging the Northern Sky Using a New Generation of Software Technology

1994 ◽  
Vol 161 ◽  
pp. 205-212
Author(s):  
N. Weir ◽  
S. Djorgovski ◽  
U. Fayyad ◽  
J.D. Smith ◽  
J. Roden
Keyword(s):  
Machine Learning ◽  
Correlation Analysis ◽  
Data Set ◽  
Test Region ◽  
Scientific Analysis ◽  
Tree Algorithms ◽  
Group A ◽  
Point Correlation ◽  
Galaxy Classification ◽  
New Generation

We have developed a system, called SKICAT, for producing, managing and analyzing catalogs from the digitized POSS-II survey. The system classifies and matches catalogs from multiple, overlapping plate scans as well as CCD calibration sequences; and it can be used for the scientific analysis of the resulting catalogs. It incorporates a number of novel machine-learning and AI tools, including the star/galaxy classification using decision tree algorithms. This results in star/galaxy separation accurate to 90% or better down to B J ∼ 21 m , i.e. ∼ 1 m above the plate limit The final catalog is expected to contain at least 5 × 107 galaxies and > 2 × 109 stars. We present preliminary results on galaxy counts from a test region near the NGP. We find a mild excess over the no-evolution models, smaller than previously found by the APM group. A search for z > 4 quasars and the two-point correlation analysis of this data set are in progress.

A multi-point correlation analysis of a crossflow-dominated boundary layer

1996 ◽  
Author(s):  
K. Chapman ◽  
M. Glauser ◽  
M. Reibert ◽  
W. Saric
Keyword(s):  
Boundary Layer ◽  
Correlation Analysis ◽  
Point Correlation

scholarly journals Cross-Correlation Analysis of Galaxies with Different Luminosity

1988 ◽  
Vol 130 ◽  
pp. 554-554
Author(s):  
X.-Y. Xia ◽  
Z.-G. Deng ◽  
Y.-Z. Liu
Keyword(s):  
Correlation Function ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Absolute Magnitude ◽  
Cross Correlation Function ◽  
Cross Correlation Analysis ◽  
Luminous Galaxies ◽  
Point Correlation ◽  
Straight Lines

In the former work (Xia, Deng and Zhou, 1986), we have showed by two- point correlation analysis that more luminous galaxies cluster stronger. Now we present the result of cross-correlation analysis for galaxies with different luminosity. This analysis supplies information about the relations between the distributions of galaxies with different luminosity. The analyses are based on the data given by CfA survey and have made the same corrections as in the former work. The samples are divided into three subgroups in absolute magnitude ranges: a) −21–22, b) −20–21 and c)−19–20. We make the cross-correlation analysis for each two subgroups. Fig. 1 gives the obtained cross-correlation function ξc(r) and Fig. 2 shows the log ξc(r)-log r diagram, the straight lines in Fig. 2 are given by linear regression. These results show that the two brightest subgroups have the strongest correlation. Combining with the results of former work, it follows that the probability of two brighter galaxies being close to each other is larger than that of fainter galaxies.

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