- 1. SIFT ML/DM Monday2013/3/11 @scwanghttp://sdrv.ms/ZsU8vG
2. Who Am I? (Shao-Chuan Wang)Currently as SDE atHad _some_
computer vision experience.Mainly speak Python and
C/C++.@scwang/in/scwangshaochuan.wang 3. (1/2) 4. (2/2)??? ??
Appearance? ? 5. Distinctiveness Invariance (robustness) 6. Affine
invariance Viewpoint Scaling Skew Rotation/orientation Translation
Occlusion ()Luminance (, 7. (e.g. )VS (histogram of pixels) ML ,
Robust model 8. Interest point/keypoints? (high information) /:
ambiguous, degeneracy :Corner/blobs are moreinteresting thanedges
and lines. 9. 1D Blobs 2D Blobs 10. 11. (Scale Space) BLOB ABLOB B
BLOB CAB CLocal Extrema in (x*, *) Space Represent Blobs 12. Blob !
13. Blob SizeCharacteristic Scale Given a 1D signal f(x). 2 ns
Compute * f (x) at many scales (0, 1, , )x2k Find: ns2(x , s ) =
arg max 2 * f (x) * * ( x,s ) x Characteristic scale 14.
Convolution I(x, y) S(x, y, s 1 )S(x, y, s 2 ) S(x, y, s 3 )
Large(x*, y*, s * ) = argmax s 2 2 ns * I(x, y) ( x,y,s )(x , y , s
) = argmax s 2 2 S(x, y, s )* * * ( x,y,s ) 15. [Mikolajczyk 2004]
16. Laplacian of Gaussian V.S. Difference of GaussianG(x, y, ss
)-G(x, y, s ) (s -1)s 22G. [Lowe 2004] 17. ? (Histogram of
gradients)Assignment of orientationPrincipal OrientationHistogram
of orientationCharacteristic Scale [Lowe 2004] 18. : (Principal
Orientation), DEMO$ git clone
https://github.com/shaochuan/MLDM-demo.git$ cd MLDM-demo/$
./pull_submod.sh$ cd sift/$ ./rotate 19. Characteristic Scale DEMO$
git clone https://github.com/shaochuan/MLDM-demo.git$ cd
MLDM-demo/$ ./pull_submod.sh$ cd sift/$ ./scale 20. : [Mikolajczyk
2004] 21. : / NAVE FLOW DEMO$ ./tracking: (Video
stabilization)(Synthetic slow motion) 22. : 3D [Pollefeys 2006] 23.
: Bag-Of-Words 24. Reference[Mikolajczyk 2004] Scale & Afne
InvariantInterest Point Detectors.[Lowe 2004] Distinctive Image
Features fromScale-Invariant Keypoints[Pollefeys 2006] Towards
Urban 3DReconstruction From Video
