SIFT keypoint detection. D. Lowe, Distinctive image features from scale-invariant keypoints, IJCV 60 (2), pp , 2004.

Transcription

1 SIFT keypoint detection D. Lowe, Distinctive image features from scale-invariant keypoints, IJCV 60 (), pp , 004.

2 Keypoint detection with scale selection We want to extract keypoints with characteristic scale that is covariant with the image transformation

3 Basic idea Convolve the image with a blob filter at multiple scales and look for extrema of filter response in the resulting scale space T. Lindeberg. Feature detection with automatic scale selection. IJCV 30(), pp , 1998.

4 Blob detection minima * = maxima Find maxima and minima of blob filter response in space and scale Source: N. Snavely

5 Blob filter Laplacian of Gaussian: Circularly symmetric operator for blob detection in D g = x g + y g

6 Recall: Edge detection f Edge d dx g Derivative of Gaussian f d dx g Edge = maximum of derivative Source: S. Seitz

7 Edge detection, Take f Edge d dx g Second derivative of Gaussian (Laplacian) d dx f g Edge = zero crossing of second derivative Source: S. Seitz

8 From edges to blobs Edge = ripple Blob = superposition of two ripples maximum Spatial selection: the magnitude of the Laplacian response will achieve a maximum at the center of the blob, provided the scale of the Laplacian is matched to the scale of the blob

9 Scale selection We want to find the characteristic scale of the blob by convolving it with Laplacians at several scales and looking for the maximum response However, Laplacian response decays as scale increases: original signal (radius=8) increasing σ

10 Scale normalization The response of a derivative of Gaussian filter to a perfect step edge decreases as σ increases 1 σ π

11 Scale normalization The response of a derivative of Gaussian filter to a perfect step edge decreases as σ increases To keep response the same (scale-invariant), must multiply Gaussian derivative by σ Laplacian is the second Gaussian derivative, so it must be multiplied by σ

12 Effect of scale normalization Original signal Unnormalized Laplacian response Scale-normalized Laplacian response maximum

13 Blob detection in D Scale-normalized Laplacian of Gaussian: g g g = σ + norm x y

14 Blob detection in D At what scale does the Laplacian achieve a maximum response to a binary circle of radius r? r image Laplacian

15 Blob detection in D At what scale does the Laplacian achieve a maximum response to a binary circle of radius r? To get maximum response, the zeros of the Laplacian have to be aligned with the circle The Laplacian is given by (up to scale): ( x + y σ ) e ( x Therefore, the maximum response occurs at + y )/ σ σ = r /. circle r 0 Laplacian image

16 Scale-space blob detector 1. Convolve image with scale-normalized Laplacian at several scales

17 Scale-space blob detector: Example

18 Scale-space blob detector: Example

19 Scale-space blob detector 1. Convolve image with scale-normalized Laplacian at several scales. Find maxima of squared Laplacian response in scale-space

20 Scale-space blob detector: Example

21 Eliminating edge responses Laplacian has strong response along edge

22 Eliminating edge responses Laplacian has strong response along edge Solution: filter based on Harris response function over neighboroods containing the blobs

23 Efficient implementation Approximating the Laplacian with a difference of Gaussians: ( (,, ) (,, )) L= Gxx x y + Gyy x y σ σ σ (Laplacian) DoG = G( x, y, kσ) G( x, y, σ) (Difference of Gaussians)

24 Efficient implementation David G. Lowe. "Distinctive image features from scale-invariant keypoints. IJCV 60 (), pp , 004.

25 From feature detection to feature description Scaled and rotated versions of the same neighborhood will give rise to blobs that are related by the same transformation What to do if we want to compare the appearance of these image regions? Normalization: transform these regions into samesize circles Problem: rotational ambiguity

26 Eliminating rotation ambiguity To assign a unique orientation to circular image windows: Create histogram of local gradient directions in the patch Assign canonical orientation at peak of smoothed histogram 0 π

27 SIFT features Detected features with characteristic scales and orientations: David G. Lowe. "Distinctive image features from scale-invariant keypoints. IJCV 60 (), pp , 004.

28 From feature detection to feature description Detection is covariant: features(transform(image)) = transform(features(image)) Description is invariant: features(transform(image)) = features(image)

29 SIFT descriptors Inspiration: complex neurons in the primary visual cortex D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV 60 (), pp , 004.

30 Properties of SIFT Extraordinarily robust detection and description technique Can handle changes in viewpoint Up to about 60 degree out-of-plane rotation Can handle significant changes in illumination Sometimes even day vs. night Fast and efficient can run in real time Lots of code available Source: N. Snavely

31 A hard keypoint matching problem NASA Mars Rover images

32 Answer below (look for tiny colored squares ) NASA Mars Rover images with SIFT feature matches Figure by Noah Snavely

33 What about 3D rotations?

34 What about 3D rotations? Affine transformation approximates viewpoint changes for roughly planar objects and roughly orthographic cameras

35 Affine adaptation R R I I I I I I y x w M y y x y x x y x = = 1 1, 0 0 ), ( λ λ direction of the slowest change direction of the fastest change (λ max ) -1/ (λ min ) -1/ Consider the second moment matrix of the window containing the blob: const ] [ = v u M v u Recall: This ellipse visualizes the characteristic shape of the window

36 Affine adaptation K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. IJCV 60(1):63-86, 004.