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Artificial Intelligence Laboratory & Laboratory for Information and Decision Systems

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.

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Artificial Intelligence Laboratory & Laboratory for Information and Decision Systems

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  1. MIT AI Lab / LIDS 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

  2. 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

  3. MIT AI Lab / LIDS MAR Processes for SAR Irving, Willsky & Novak Pyramid Residuals

  4. MIT AI Lab / LIDS Intuition: Construct a Model for the Scale-to-scale Dependency in SAR imagery L0 Parent Vector L1 V(x,y)={} L2 L3 L4

  5. MIT AI Lab / LIDS Build a Model for Observed Distribution IWN: Conditionally Gaussian

  6. MIT AI Lab / LIDS

  7. 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)

  8. MIT AI Lab / LIDS Steerable Pyramids Freeman and Simoncelli

  9. MIT AI Lab / LIDS …for a SAR image

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

  11. MIT AI Lab / LIDS Build a Model for Observed Distribution DB: Non-parametric Distribution

  12. MIT AI Lab / LIDS Probabilistic Model Markov Conditionally Independent Successive Conditioning

  13. MIT AI Lab / LIDS Estimating Conditional Distributions • Non-parametrically

  14. MIT AI Lab / LIDS Outline synthesis sample registration discrimination distribution Likelihood Similarity example image segmentation denoising distribution condition super resolution

  15. MIT AI Lab / LIDS Capturing Structure (Texture Perspective)

  16. MIT AI Lab / LIDS Synthesis Results

  17. MIT AI Lab / LIDS Synthesis Results

  18. MIT AI Lab / LIDS

  19. MIT AI Lab / LIDS Alternative 1: Gaussian Distribution: GMRF Chellappa and Chattergee

  20. MIT AI Lab / LIDS

  21. MIT AI Lab / LIDS Alternative 2: Statistical Wavelet Models Bergen and Heeger Donoho Simoncelli and Adelson

  22. MIT AI Lab / LIDS Heeger and Bergen Texture Synthesis Model

  23. MIT AI Lab / LIDS Heeger and Bergen Texture Synthesis Model

  24. MIT AI Lab / LIDS Analysis Synthesis Sampling Procedure original texture patch synthesized texture patch

  25. MIT AI Lab / LIDS Not quite right... Very similar to a Gaussian Model (i.e. no phase alignment)

  26. MIT AI Lab / LIDS Wavelet Representation of Edges Wavelet Transform

  27. MIT AI Lab / LIDS Pyramid Representation

  28. MIT AI Lab / LIDS Conditional Distributions Wavelet Transform

  29. MIT AI Lab / LIDS Analysis Synthesis Sampling Procedure synthesized texture patch original texture patch

  30. MIT AI Lab / LIDS Multiresolution progression

  31. MIT AI Lab / LIDS

  32. MIT AI Lab / LIDS Joint feature occurrence across resolution

  33. MIT AI Lab / LIDS Joint feature occurrence across resolution

  34. MIT AI Lab / LIDS

  35. MIT AI Lab / LIDS

  36. MIT AI Lab / LIDS Texture Synthesis Results

  37. 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 17degrees (targets were at 15 degrees)

  38. 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 roughly 200 images in each class. BMP2-9563 BMP2-9566 T72-812 T72-S7

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

  40. MIT AI Lab / LIDS BMP2-C21 BTR70-C71 T72-132 Flexible Histograms Template Matching De Bonet, Fisher and Viola

  41. MIT AI Lab / LIDS Measuring Visual Structure : Flexible Histogram II Rtie-point B (x,y)= 8 Rtest parent structure

  42. MIT AI Lab / LIDS Measuring Visual Structure : Flexible Histogram III Rlandmark B(,x,y)= 8 Rtie-point 2= (B-B’)2/B B’(x,y)= 3 Rtest

  43. MIT AI Lab / LIDS Tie-point determination Multiresolution alignment search Multiresolution texture match: flexible histograms Registration pipeline

  44. MIT AI Lab / LIDS Tie-point determination

  45. MIT AI Lab / LIDS Tie-point examples Here, only vehicles provide distinct landmarks. When present, roads and buildings provide useful landmarks as well.

  46. MIT AI Lab / LIDS Coarse to fine alignment Fine Coarse

  47. MIT AI Lab / LIDS Example Registration

  48. MIT AI Lab / LIDS Example Registration

  49. MIT AI Lab / LIDS Example Registration

  50. MIT AI Lab / LIDS

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