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The Terrapins

The Terrapins. Computer Vision Laboratory University of Maryland. Yi Li. Cornelia Ferm ü ller. Justin Domke. Yiannis Aloimonos. Kostas Bitsakos. Michi Nishigaki. and some others. Xiaodong Yu. Alap Karapurkar. A challenge for Understanding Vision. Navigation Recognition Memory

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The Terrapins

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  1. The Terrapins Computer Vision Laboratory University of Maryland

  2. Yi Li Cornelia Fermüller Justin Domke Yiannis Aloimonos Kostas Bitsakos Michi Nishigaki and some others Xiaodong Yu Alap Karapurkar

  3. A challenge for Understanding Vision • Navigation • Recognition • Memory • Active Vision

  4. The approach • Navigation • Recognition

  5. Navigation • Build map using SLAM from laser Set way points

  6. Recognition Strategy • Proper Nouns: discriminative features and geometric transform very few internet images (3) • General Nouns: shape descriptor 20-30 internet images

  7. SIFT • Dense feature points • Usually correct matches • Poor at too much distortion

  8. MSERs • Sparse keypoints • Usually correct matches • Great at affine invariance

  9. Recognition Strategy for Proper Nouns Feature matching using SIFT Compute Homography Image Match with MSER (affine invariant) Unwarp using Edges Examples

  10. Initial matches Homography Final matches The matching Process

  11. Initial matches Homography Final matches Another Example

  12. Matches of planar objects

  13. General nouns • Segmentation for the ground from trinocular stereo for the upper camera from color • Shape description using adjacent line segments

  14. Segmentation from depth information Estimate the transformation of the ground plane between the different cameras

  15. Estimation of the ground plane homography

  16. The descriptor • Fit edges to small lines • Adjacent lines: encode the relative coordinates w.r.t pivot point. • C / Z shape • Y shape

  17. The codebook for the descriptor • The advantage of the codebook • Generic • Quantization -> fast • generate the codebook • A large dataset • Extract descriptor • Cluster the descriptor

  18. Classifier: Support Vector Machine Suppose we have N classes For each class, we train 1 SVM using images from this class vs other classes. Result: N SVM classifiers (linear classifier in high dimensional space)

  19. Example: Apply this descriptor to natural images

  20. Future steps • Taking images: Segmentation into surfaces. Combine geometry (local occlusion information from motion and/or stereo) with edge information • Recognition: surface boundaries, symmetry information

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