Artificial Intelligence Laboratory & Laboratory for Information and Decision Systems
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MIT AI Lab / LIDS Artificial Intelligence Laboratory & Laboratory for Information and Decision Systems Massachusetts Institute of Technology A Unified Multiresolution Framework for Automatic Target Recognition Eric Grimson, Alan Willsky, Paul Viola, Jeremy S. De Bonet, and John Fisher
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A Unified Multiresolution Framework for Automatic Target Recognition. Eric Grimson, Alan Willsky, Paul Viola, Jeremy S. De Bonet, and John Fisher. Artificial Intelligence Laboratory & Laboratory for Information and Decision Systems Massachusetts Institute of Technology. Outline. - PowerPoint PPT Presentation
Transcript of Artificial Intelligence Laboratory & Laboratory for Information and Decision Systems
MIT AI Lab / LIDS
Artificial Intelligence Laboratory &Laboratory for Information and Decision SystemsMassachusetts Institute of Technology
A Unified Multiresolution Framework for
Automatic Target Recognition
Eric Grimson, Alan Willsky, Paul Viola, Jeremy S. De Bonet, and John Fisher
MIT AI Lab / LIDS
Outline
• Review Multiresolution Analysis Models– MAR (Multiresolution Auto-Regressive)
– MNP (Multi-scale Nonparametric)
• Applications of MNP Models– Classification/Recognition
– Segmentation and Multi-Look Registration
– Synthesis and Super-Resolution
• Continuing Efforts
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MAR Processes for SAR
Pyramid Residuals
Irving,Willsky &Novak
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Intuition: Construct a Model for the Scale-to-scale Dependency in SAR imagery
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Build a Model for Observed Distribution
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MIT AI Lab / LIDS
MIT AI Lab / LIDS
Multi-scale Non-parametric Models
• Two key insights:– Alternative multi-scale representation
• Sub-band oriented representations (Wavelets, Gabor Filters)
– Non-parametric models of conditional dependence
De Bonet & Viola (1997)
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Freeman and Simoncelli
Steerable Pyramids
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…for a SAR image
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MIT AI Lab / LIDS
Build a Model for Observed Distribution
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MIT AI Lab / LIDS
Probabilistic Model
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MIT AI Lab / LIDS
Estimating Conditional Distributions
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distribution Similarity
sample
Likelihood
distribution condition
exampleimage
synthesis
discrimination
registration
segmentation
denoising
super resolution
Outline
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Capturing Structure (Texture Perspective)
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Synthesis Results
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Synthesis Results
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MIT AI Lab / LIDS
Alternative 1: Gaussian Distribution: GMRF
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MIT AI Lab / LIDS
Alternative 2: Statistical Wavelet Models
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Heeger and Bergen Texture Synthesis Model
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Heeger and Bergen Texture Synthesis Model
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Analysis Synthesis
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Not quite right...
Very similar to a Gaussian Model(i.e. no phase alignment)
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Wavelet Representation of Edges
WaveletTransform
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Pyramid Representation
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Conditional Distributions
WaveletTransform
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orig
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text
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Sam
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g P
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dure
Analysis Synthesis
synt
hesi
zed
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Multiresolution progression
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MIT AI Lab / LIDS
Joint feature occurrence across resolution
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Joint feature occurrence across resolution
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MIT AI Lab / LIDS
MIT AI Lab / LIDS
Texture Synthesis Results
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BMP2-C21 BTR70-C71 T72-132
Models
Models for target vehicles were generated from example images:
• generated from vehicles with different numbers from the target vehicles• only 10 examples, evenly distributed in heading angle• measured at a depression angle of 17 degrees (targets were at 15 degrees)
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BMP2-9563 BMP2-9566
BTR70-C71
T72-812 T72-S7
Target vehicles
• Five target vehicles were used.
• Vehicles which differed from the target class were included as confusion targets.
• There were roughly 200 images in each class.
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2S1 BRDM2 D7 T62
ZIL131 ZSU23
Confusion vehicles
Six additional confusion vehicles were used as well.
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BMP2-C21 BTR70-C71 T72-132
Flexible Histograms
Template Matching
De Bonet, Fisher and Viola
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Rtie-point
Rtest
parent structure
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Measuring Visual Structure : Flexible Histogram II
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Rlandmark
B(,x,y)= 8
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Rtie-point
Rtest
Measuring Visual Structure : Flexible Histogram III
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Registration pipeline
Tie-pointdetermination
Multiresolution texture match:flexible histograms
Multiresolutionalignmentsearch
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Tie-point determination
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Here, only vehicles provide distinct landmarks.
When present, roads and buildings provide useful landmarks as well.
Tie-point examples
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Coarse Fine
Coarse to fine alignment
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Example Registration
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Example Registration
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Example Registration
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MIT AI Lab / LIDS
MIT AI Lab / LIDS
MIT AI Lab / LIDS
distribution | | condition
Prior beliefs about natural images
image analysis
Image + Noise
maximum likelihood sample
Blind Image Denoising
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MIT AI Lab / LIDS
MIT AI Lab / LIDS
