an automated image processing system for rpi data ivan galkin, bodo reinisch, grigori khmyrov,...
DESCRIPTION
IMAGE Team Meeting Lowell, June 5, RPI Dataset 700,000 plasmagrams collected by June 2003 At 2 sec/image ~ 400 hrs to just glance at each imageTRANSCRIPT
An Automated Image Processing System for RPI Data
Ivan Galkin, Bodo Reinisch, Grigori Khmyrov, Alexander Kozlov, Xueqin Huang,
Robert Benson, Shing Fung
IMAGE Science Working Group Meeting Lowell, Massachusetts June 5, 2003
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Outline• RPI Dataset and Intelligent Systems for
Automated Data Exploration• CORPRAL Development
– Resonance matching– Trace selection
• CORPRAL Results– RPI Database for Level-2 data products– Recent statistics– Future work
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RPI Dataset
• 700,000 plasmagrams collected by June 2003• At 2 sec/image ~ 400 hrs to just glance at
each image
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CORPRALVenku 1 Bill 1 Robert 1 Alexander 2 Vance 5 Bodo 5 Cindy 12 Patrick 20 Shing 40 Gary 60 Don 62 Marie 63 Ivan 69 Maria 119 Grigori 167 Christopher 477 Qiang 506 CORPRAL V1 97733 Total 97985
CognitiveOnlineRpiPlasmagramRankingALgorithm
Level 2 dataset, rated plasmagrams
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Access to CORPRAL Data
Lowelldatabase
SMOC
Processing logs
CORPRAL ratings
Plasmagram of the day
http://umlcar.uml.eduResonance processing in BinBrowser
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New in CORPRAL
• Resonance matching• Improvements in the vision model
– Zones of facilitation pattern– Perceptual grouping
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RPI Plasmagram
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Resonance Model1. Gyrofrequency fce and
its harmonics nfce
2. Plasma frequency fpe
3. Upper Hybrid resonance, fT
4. Q-type and its harmonics, fQn (a.k.a. Bernstein mode resonances)
5. D-type and its harmonics, fDn
22cepeT fff
2
2
2
46.0
ce
peceQn f
fn
nff
22
22
95.0
ceDnDn
ceDnDn
cepeDn
fff
fff
nfff
fpe and fce drive all other frequencies
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Previous Approach• Superimpose a comb template on the
plasmagram and seek the best quality of fit for varying drivers fpe and fce
• Used for ISIS/Alouette, ISS-B, ISEE-1• Discouraging results with RPI data
– Tremendous variety of conditions– Frequency coverage not always optimal– Accuracy vs precision issues
• 0.7% accuracy is required for model self-consistency… often means subpixel accuracy
– Noise environment
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Improvements
A. Image filter to highlight resonance signatures.
B. Resonance signature detection and contrast evaluation. Use signature contrasts to calculate fit quality.
C. Account for changes in the medium within measurement time.
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A. Signature FilterF(Ai) = median{Aj}, j = (1, i)(“cumulative” median filter)
Filter response to a falling envelope
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Filter response to a rising envelope
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A. Filter Example
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B. Signature Detector
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C. Change of Medium
fce(t1) fpe (t2) fT (t3)
)()()(
)()()(
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3
12
22
3
tftftf
tftftf
cepeT
cepeT
• Driving fce and fpe are taken at the plasmagram start.• Template frequencies are corrected for the
gradients of the driving fce and fpe .
• Iterative scheme is used to find self-consistent set of all involved resonance frequencies.
Time, frequency
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Resonances in BinBrowser
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Confidence Level• Confidence of automated
resonance scaling = Nu, number of signatures remained unmatched to their theoretical counterparts
• Nu is written as “expert rating” to the RPI Level 2 database
• Queries of Nu and Nu datamining
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Plasmagram Processing
Raw ImageEchoes
TracesClassified Traces
Decisions
RotorsSaliency Map
(Mar
r’s P
arad
igm
)
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Raw Image Echoes
RAW IMAGE
LABELING (NO THRESHOLDING)
LABELING (AFTER THRESHOLDING)
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Echoes Rotors
• Rotors – contour direction at each labeled pixel
• Original estimate of the direction is local, subject to errors
• Hough Transform for initial rotor directions
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Rotors Saliency Map• Energy minimization algorithm to find optimal
rotor orientations in a dynamic system of collectively interacting rotors
• Hopfield neural network with simulated annealing scheme to find the optimal state of rotors