Image Registration for NIF Optics Inspection · 2007-07-26 · Image Registration for NIF Optics...

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.

camera at target chamber centerfocuses on each of the final optics

Final Optics Inspection Setup

Current Final Optics Registration

In-focus

Out-of-focus

Out-of-focus

Object growing?

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

?

Registration Problem Characteristics

• 35MB images

• Lots of noise!

•Transformation type:

outliers perturbation errors

current future

Choosing Main Registration Approach

• Use whole image data directly

• Use features extracted from image

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

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

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!

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

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

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

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