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Image Registration for NIF Optics Inspection
Presentation toCASIS, Signal and Imaging Sciences Workshop
Judy Liebman, Laura Kegelmeyer, Marijn Bezuijen
November 18-19, 2004
UCRL-PRES-208127
This work was performed under the auspices of the U.S. Department of Energy by University ofCalifornia, Lawrence Livermore National Laboratory under Contract.
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camera at target chamber centerfocuses on each of the final optics
Final Optics Inspection Setup
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Current Final Optics Registration
In-focus
Out-of-focus
Out-of-focus
Object growing?
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Future Registration Adventures
• Optic lifetime reporting – follow optichealth through online and offline imaging
Current inspection
Previous inspection
Initial offline inclusion map
?
Initial offline damage map
?Future offlinedamage map
?
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Registration Problem Characteristics
• 35MB images
• Lots of noise!
•Transformation type:
outliers perturbation errors
current future
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Choosing Main Registration Approach
• Use whole image data directly
• Use features extracted from image
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1. Feature Detection
2. Feature
3. Finding Optimal Transformation
Standard Feature-Based Registration Steps
(xa, ya)(xa, ya)(xa, ya)(xa, ya)(xa, ya)
?
A B
Find t g T to minimize: t(A) - B
(xb, yb)(xb, yb)(xb, yb)(xb, yb)(xb, yb)
Matching
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2) Feature Matching
• Challenge: high % of outlying points leads to breakdownpoint
• Expecting a small transformation?• Distances between features in different sets < distances between adjacentfeatures in single set?
• Evaluate guess using expected matching %
• For larger transformations, reduce feature density using apriori knowledge
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Feature Registration Without Feature Matching?
• Points vote to concur on thebest transformation
• Comparing two points onlysupplies x and y shift
• Similar feature consensusand clustering techniquesuse complex features to findrotation, scaling and shifts
• Adapt to use pairs ofpoints from each image?
Point setA
Point setB
One pointvotes for allpossible shifts
Incrementtransform spacewith votes
Find and apply peak shifts!
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Current Final Optics Solution: Iterative RegistrationPoint set A Point set B
1. Use voting method toroughly find large shiftsbetween images
2. Use featuremeasurements andlocations to find optimalmatch
Optics through focus
Measurementsof single
feature
Dates of inspections
Measurementsof single
feature
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Probabilistic Transformation Without Feature Matching
1. Choose random transformation
2. Evaluate transformation
3. Decide whether to accept new position• If evaluation sum > previous accept• If evaluation sum < previous accept with low probability
4. Iterate
01Distribution of Offsets
Distribution of Magnifications
Pointset A
Pointset B
0Distribution of Rotations
Developed by Marijn Bezuijen
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Conclusion: Registration Now and Then
1. Vote for largeshift
2. Matching & leastsquares findssmall additionalshifts andmagnification
1. Vote for completetransformation usingsubset of features
2. Probabilistic method:midsize transformationusing all features
3. Matching & leastsquares finds smalloptimal transformation
Evaluate ResultsEvaluate Results
Evaluate Results
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3) Finding Optimal Transformation
Finding shifts and scaling is a linear & separable problem:
•Two point sets:
• Want to find optimal scaling and shifting in each direction:
• To include rotation implement analytic least squares solution
(xa1, ya1)
(xa2, ya2)
(xa3, ya3)
A B(xb1, yb1)
(xb2, yb2)
(xb3, yb3)
Ax = b
xa1 1
xa2 1
xa3 1
xa4 1
x_scaling
x_shift =
xb1
xb2
xb3
xb4
x_scaling
X_shift = (ATA)-1AT b