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Aligning Time Lapses of Stars Using Contextual Information

Aligning Time Lapses of Stars Using Contextual Information. By Holly Chu and Justin Hoogenstryd Academic Advisor Ernie Esser Uci math department. Introduction . Time lapse video of stars rotating around the North Star, Polaris. [1] Video stabilization problem

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Aligning Time Lapses of Stars Using Contextual Information

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  1. Aligning Time Lapses of Stars Using Contextual Information By Holly Chu and Justin Hoogenstryd Academic Advisor Ernie Esser Uci math department

  2. Introduction • Time lapse video of stars rotating around the North Star, Polaris.[1] • Video stabilization problem • Achieve rotationally invariant descriptor to match and automatically align frames that are significantly rotated. • Reappearing/disappearing stars • Consistent choosing of points • [1] http://www.youtube.com/watch?v=xiasktkjc9m.

  3. background • Log-Polar Bins masked onto image[3] • Context Histogram displays intensities, useful in comparison • [3] J. Malik S. Belongie and J. Puzicha. Shape Matching and Object Recognition Using Shape Contexts, volume Vol. 24. IEEE Trans, 2002.

  4. Frame alignment • Utilized corner detect • Each frame interpolated onto the previous neighboring frame • Using global contextual descriptor, rotational invariance unnecessary

  5. Corner detect • Corner detector approach • Drawback: will not necessarily find the same point on the same star every time leading to slightly blurry alignments.

  6. Our Approach • Corner detect approach was computationally intensive, blurred data • Finding star centroids worked faster, produced more clear results • Centroid of brightest stars • K-means

  7. Cumulative alignment of frames • Compute transformation matrix for neighboring frame and next one. • Multiplied transformation matrices of frame 2 through frame N

  8. Cumulative alignment of frames limitations • Frame alignment between frame 1 and frame 1000 • Visualization of point correspondences on Frame 1 and Frame 1000 results • Rotational Invariance with FFT

  9. Normal context histograms • Rotation of the points causes a period shift in each row of the histogram

  10. Rotational invariance using FFT • Magnitude of Fourier coefficients are unaffected by periodic shifts. • Discrete Fourier Transform[2] • [2] J.W. ; Favin D.L. ; Helms H.D. ; Kaenel R.A. ; Lang W.W. ; Maling G.C. Jr. ; Nelson D.E. ; Rader C.M. ; Welch P.D. Cochran, W.T.Cooley. Cochran et al.: The fast fourier transform. 1967.

  11. Rotationally invariant context histograms • Altered star intensity detector

  12. Comparison

  13. Rotationally invariant frame demo • Frame 1 and Frame 1 rotated 90 degrees • Rotationally invariant Frame 1 and Frame 1 rotated 90 degrees • Frame 250 and Frame 500 • Rotationally invariant Frame 250 and Frame 500

  14. Future work • Apply descriptor to longer videos • Incorporate illumination invariance • Ensure that point correspondences are consistent with estimated deformation. • Compare with existing methods • Account for mismatches due to boundary issues

  15. Bibliography • [1] http://www.youtube.com/watch?v=xiasktkjc9m. • [2] J.W. ; Favin D.L. ; Helms H.D. ; Kaenel R.A. ; Lang W.W. ; MalingG.C. Jr. ; Nelson D.E. ; Rader C.M. ; Welch P.D. Cochran, W.T.Cooley. Cochran et al.: The fast fourier transform. 1967. • [3] J. Malik S. Belongie and J. Puzicha. Shape Matching and Object Recognition Using Shape Contexts, volume Vol. 24. IEEE Trans, 2002.

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