1 / 24

Jianke Zhu From Haibin Ling’s ICCV talk

Fast Marching Method and Deformation Invariant Features. Jianke Zhu From Haibin Ling’s ICCV talk. Outline. Introduction Fast Marching Method Deformation Invariant Framework Experiments Conclusion and Future Work. General Deformation. One-to-one, continuous mapping.

luna
Download Presentation

Jianke Zhu From Haibin Ling’s ICCV talk

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fast Marching Method and Deformation Invariant Features Jianke Zhu From Haibin Ling’s ICCV talk

  2. Outline • Introduction • Fast Marching Method • Deformation Invariant Framework • Experiments • Conclusion and Future Work

  3. General Deformation • One-to-one, continuous mapping. • Intensity values are deformation invariant. • (their positions may change)

  4. Our Solution • A deformation invariant framework • Embed images as surfaces in 3D • Geodesic distance is made deformation invariant by adjusting an embedding parameter • Build deformation invariant descriptors using geodesic distances

  5. Related Work • Embedding and geodesics • Beltrami framework [Sochen&etal98] • Bending invariant [Elad&Kimmel03] • Articulation invariant [Ling&Jacobs05] • Histogram-based descriptors • Shape context [Belongie&etal02] • SIFT [Lowe04] • Spin Image [Lazebnik&etal05, Johnson&Hebert99] • Invariant descriptors • Scale invariant descriptors [Lindeberg98, Lowe04] • Affine invariant [Mikolajczyk&Schmid04, Kadir04, Petrou&Kadyrov04] • MSER [Matas&etal02]

  6. Outline • Introduction • Deformation Invariant Framework • Intuition through 1D images • 2D images • Experiments • Conclusion and Future Work

  7. 1D Image Embedding 1D Image I(x) EMBEDDING I(x)  ( (1-α)x, αI ) (1-α)x αI Aspect weight α: measures the importance of the intensity

  8. q p Geodesic Distance • Length of the shortest path along surface αI g(p,q) (1-α)x

  9. Geodesic Distance and α I1 I2 embed embed Geodesic distance becomes deformation invariant for α close to 1

  10. Embedded Surface Curve on Length of Image Embedding & Curve Lengths ImageI Take limit Depends only on intensity I Deformation Invariant

  11. Δ Δ Δ Δ Δ Deformation Invariant Sampling Geodesic Sampling • Fast marching: get geodesic level curves with sampling interval Δ • Sampling along level curves with Δ p sparse dense

  12. p q intensity intensity geodesic distance geodesic distance Deformation Invariant Descriptor Geodesic-Intensity Histogram (GIH) p q

  13. Real Example p q

  14. Deformation Invariant Framework Image Embedding ( close to 1) Deformation Invariant Sampling Geodesic Sampling Build Deformation Invariant Descriptors (GIH)

  15. Practical Issues • Lighting change • Affine lighting model • Normalize the intensity • Interest-Point • No special interest-point is required • Extreme point (LoG, MSER etc.) is more reliable and effective

  16. Invariant vs. Descriminative

  17. Outline • Introduction • Deformation Invariance for Images • Experiments • Interest-point matching • Conclusion and Future Work

  18. Data Sets Synthetic Deformation & Lighting Change (8 pairs) Real Deformation (3 pairs)

  19. Interest-Points Interest-point Matching • Harris-affine points [Mikolajczyk&Schmid04] * • Affine invariant support regions • Not required by GIH • 200 points per image • Ground-truth labeling • Automatically for synthetic image pairs • Manually for real image pairs * Courtesy of Mikolajczyk, http://www.robots.ox.ac.uk/~vgg/research/affine/

  20. Descriptors and Performance Evaluation Descriptors • We compared GIH with following descriptors: Steerable filter [Freeman&Adelson91], SIFT [Lowe04], moments [VanGool&etal96], complex filter [Schaffalitzky&Zisserman02], spin image [Lazebnik&etal05] * Performance Evaluation • ROC curve: detection rate among top N matches. • Detection rate * Courtesy of Mikolajczyk, http://www.robots.ox.ac.uk/~vgg/research/affine/

  21. Synthetic Image Pairs

  22. Real Image Pairs

  23. Outline • Introduction • Deformation Invariance for Images • Experiments • Conclusion and Future Work

  24. Thank You!

More Related