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Robust Localization, Classification, and Temporal Evolution Tracking in Auroral Data

Robust Localization, Classification, and Temporal Evolution Tracking in Auroral Data. G. Germany * C.C. Hung † T. Newman * J. Spann ‡ * Univ. of Alabama in Huntsville † S. Polytechnic State Univ. ‡ MSFC. AISR Meeting, April 4-6, 2005. Visual Perception I. What do you see?.

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Robust Localization, Classification, and Temporal Evolution Tracking in Auroral Data

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  1. Robust Localization, Classification, and Temporal Evolution Tracking in Auroral Data G. Germany * C.C. Hung † T. Newman * J. Spann ‡ * Univ. of Alabama in Huntsville † S. Polytechnic State Univ. ‡ MSFC AISR Meeting, April 4-6, 2005

  2. Visual Perception I • What do you see? from Torrans 99 (GMU) from Schiffman’s Sensation and Perception, 2000, as presented by Loken et al., Macalester Coll.

  3. Visual Perception I • What do you see? from Torrans 99 (GMU) from Schiffman’s Sensation and Perception, 2000, as presented by Loken et al., Macalester Coll. Gestalt = Form, a unit of perception Note: Avoid strict Gestaltism!

  4. Visual Perception II: Some Gestalt • Continuity • Closure • Proximity • Similarity H ppy Birthday EEEEEEEEEEFEEEEEEEEEE

  5. A Problem: Finding Auroral Oval UVI Image Region-Based Seg.

  6. A Problem: Finding Auroral Oval UVI Image Region-Based Seg. * Incomplete

  7. Toward a Better Solution? • Exploit: • Continuity • Closure • Proximity • Similarity

  8. Toward a Better Solution? • Exploit: • Continuity • Closure • Proximity • Similarity

  9. General Goal: Support Study of Aurora • UVI Polar Datasets • 9M+ images! • Exploitation Challenge • OST (Germany Talk @ AISR05) • Queries based on: • Sensor Location • Some Image Features: • Integrated Intensity • Boundary Position • Image Quality • Popular: • 1000/100

  10. Specific Goal: Effective Retrieval • UVI OST: Mining Pre-Processing • Removes Non-Auroral Features • Extracts Auroral Oval • Inefficient • Often fails • Support Auroral Feature Search • Shape-Based Processing to Localize Oval • Better-support Auroral Feature Retrieval

  11. Shape Recovery • Our prior work: • Spheres • Cylinders • Cones • Ellipsoids • Paraboloids • Hyperboloids • Compound Structures

  12. Exploiting Shape I • Exploiting Shape: Preliminaries • What Shape? • Shape Invariant Descriptors

  13. Exploiting Shape II: Experiments • Shape Testing • Manual Tracing • Linear Least Squares Fitting • Shape Invariant Extraction

  14. Some Fitting Results Colorized Image Manually traced boundary Fittings Invar. ∆ = 2E-10 J = 1E-9 I = -8E-5 ∆/I < 0 97/011/053422 ∆ = 9E-9 J = 2E-8 I = -3E-4 ∆/I < 0 97/011/094246

  15. Initial Work: Localization • Hough-Based Processing • Democratic: • Pixels Vote • Example • y = mx + b y=px+q y=ax+c y=dx+e y=fx+g PixelA

  16. Approx. Auroral Oval Boundary • Thresholding with density examination • Thresholding value:  (mean of the pixel intensities) • LoG edge detection 99/006/093006 Thresholded image Dense part Boundary

  17. Shape-based Detection • Mod. Randomized Hough Transf. (MRHT) for ellipse: • Randomly Select Eedgels • Fit General Quadratic • Determine Shape • Ellipse Parameter Recovery (next page) • Parameter Space Decomposition • 2D accumulate array for center • 2D accumulate array for axes • 1D accumulate array for orientation

  18. Shape-based Detection (cont.) • MRHT for ellipse • Recover: • center orientation axes • where

  19. Shape-based Detection (cont.) • Separation of inner and outer boundary • Based on distance to center of detected ellipse from boundary pixels Green = detected ellipse from boundary pixels. Red = inner boundary. Blue = outer boundary 99/006/093006

  20. Shape-based Detection (cont.) • Shape detection of inner and outer boundary. 99/006/093006 Green = RHT ellipses. Red = Inner. Blue= outer boundaries of the aurora arc. 99/006/070027

  21. More Experimental Results 97/011/094246 96/344/233133 97/010/150934 99/006/213123

  22. Experimental Configuration • Machine: • CPU: 2.3 GHz • Memory: 512 MB • OS: Windows XP • Run time (wall clock) • 3 MRHT runs each with 100,000 fittings • One run for center estimation, two runs for inner and outer boundary detection •  45 seconds for each image

  23. Conclusion • Shape-Based Fitting • Goal: Better-Localizing Auroral Oval • Upcoming: • Couple w/ k-means (Hung and Germany, 2003) • Longer Term: • Auroral Classification • Temporal Evolution Tracking

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