1. A Unified Multiresolution Framework for Automatic Target Recognition
MIT AI Lab / LIDS
A Unified Multiresolution Framework
for
Automatic Target Recognition
Laboratory for Information and Decision Systems &
Artificial Intelligence Laboratory
Massachusetts Institute of Technology

2. Meeting Agenda Overview (John)
MIT AI Lab / LIDS
Meeting Agenda
Overview (John)
Multi-scale nonparametric analysis (Paul)
Extensions to MNP approach (Alan)
Discussion

3. Overview Convergence of two multiscale analysis methods Current Status
MIT AI Lab / LIDS
Overview
Convergence of two multiscale analysis methods
Multiresolution Auto-Regressive Models
Irving, Willsky
Multi-scale Nonparametric Progression
de Bonet, Viola
Current Status
Continuing Efforts

4. Research Agenda Developing class probabilistic models
MIT AI Lab / LIDS
Research Agenda
Developing class probabilistic models
capturing variability
Developing robust models and associated feature extraction algorithms
saliency, minimal descriptions
Developing computationally fast algorithms for large problems

5. Technical Approaches Sampling and nonparametric estimators
MIT AI Lab / LIDS
Technical Approaches
Sampling and nonparametric estimators
allows for modeling complex dependencies
emphasis on multi-resolution approaches
Sparse/robust representations
phenomenology
basis pursuit

6. Estimating dependencies across a multi-scale representation
MIT AI Lab / LIDS
Estimating dependencies across a multi-scale representation
Structure is modeled as a process that evolves in scale
Issues
complexity of the dependency
robustness with we can capture and exploit the dependency
low conditional entropy within the representation

7. Multiresolution parent vector
MIT AI Lab / LIDS
Multiresolution parent vector
Parent Vector
V(x,y)={ }
coarse
fine

8. Extracting a Distribution of Parent Vectors
MIT AI Lab / LIDS
Extracting a Distribution of Parent Vectors

9. distribution condition
MIT AI Lab / LIDS
From a sample image we can infer a distribution of a multi-scale process...
…from which
synthesis
sample
registration
discrimination
distribution
Likelihood
Similarity
example
image
segmentation
denoising
distribution condition
super resolution

10. Current Status Emphasis has been on classification
MIT AI Lab / LIDS
Current Status
Emphasis has been on classification
testing almost exclusively with MSTAR public release data
Completed 3-class testing on initial public release data
BTR-70, BMP-2, T-72
2S-1, BRDM-2, D-7, T-62, ZIL-131, ZSU-23
models generated at 17 degrees, tested at 15 degrees
over 2500 vehicle chips tested

11. MIT AI Lab / LIDS
Models
BMP2-C21
BTR70-C71
T72-132
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)

12. Target vehicles BTR70-C71 Five target vehicles were used.
MIT AI Lab / LIDS
BTR70-C71
Target vehicles
Five target vehicles were used.
Vehicles which differed from the target class were included as confusion targets.
There were images in each class.
BMP2-9563
BMP2-9566
T72-812
T72-S7

13. Confusion vehicles ZIL131 ZSU23
MIT AI Lab / LIDS
ZIL131
ZSU23
Confusion vehicles
Six additional confusion vehicles were used as well.
2S1
T62
BRDM2 D7

14. Multiscale Nonparametric Approach
MIT AI Lab / LIDS
BMP2-C21
BTR70-C71
T72-132
Multiscale Nonparametric Approach
Template Matching

15. MIT AI Lab / LIDS
Continuing Efforts
Inclusion of T-72 variants from MSTAR public release
IU Workshop
Use of sub-aperture/sub-band features
IU Workshop (sub-aperture)
SPIE Aerosense
Inclusion of attributed scattering center ?