1. SURF: Speeded-Up Robust Features
CPSC 643, Presentation 2
SURF: Speeded-Up Robust Features
Herbert Baya, Andreas Essa, Tinne Tuytellaresb, Luc Van Goola,b
aETH Zurich bK. U. Leuven, ESAT-PSI
Sternwartstrasse Kasteel Arenberg 10
CH-8092 Zurich B-3001 Leuven
Switzerland Belgium
Computer Vision and Image Understanding (CVIU)
Vol. 110, No. 3, pp , 2008.

2. Mostly Related Works Harris Corner Detector - Harris 1988
Laplacian of Gaussian - Lindeberg 1998
Harris - Laplace Detector - Mikolajczyk 2001
Difference of Gaussian - Lowe 2004

3. Mostly Related Works Harris Corner Detector - Harris 1988
Laplacian of Gaussian - Lindeberg 1998
Harris - Laplace Detector - Mikolajczyk 2001
Difference of Gaussian - Lowe 2004

4. Mostly Related Works Harris Corner Detector - Harris 1988
Laplacian of Gaussian - Lindeberg 1998
Harris - Laplace Detector - Mikolajczyk 2001
Difference of Gaussian - Lowe 2004

5. Mostly Related Works Harris Corner Detector - Harris 1988
Laplacian of Gaussian - Lindeberg 1998
Harris - Laplace Detector - Mikolajczyk 2001
Difference of Gaussian - Lowe 2004

6. Mostly Related Works Harris Corner Detector - Harris 1988
Laplacian of Gaussian - Lindeberg 1998
Harris - Laplace Detector - Mikolajczyk 2001
Difference of Gaussian - Lowe 2004

7. Other Related Works Salient Region Detector - Kadir 2001
Edge-based Region Detector - Jurie 2004

8. Motivation
Using Laplacian of Gaussian, one could obtain scale invariant features.
Lowe uses difference of Gaussian to approximate Laplacian of Gaussian.
This paper uses Hessian - Laplacian to approximate Laplacian of Gaussian, to improve calculation speed.

9. Methodology Using integral images for major speed up
Integral Image (summed area tables) is an intermediate representation for the image and contains the sum of gray scale pixel values of image.

10. Detection Hessian-based interest point localization
Lxx(x,y,σ) is the Laplacian of Gaussian of the image.
It is the convolution of the Gaussian second order derivative with the image.
Lindeberg showed Gaussian function is optimal for scale-space analysis.
This paper use Dxx to approximate Lxx.

11. Detection Approximated second order derivatives with box filters.
Scale analysis with constant image size

12. Description Orientation Assignment x response y response
Side length = 4s
Cost 6 operation to
compute the response
Circular neighborhood of
radius 6s around the interest point
(s = the scale at which the point was detected)

13. Description Dominant orientation
The Haar wavelet responses are represented as vectors
Sum all responses within a sliding orientation window covering an angle of 60 degree
The two summed response yield a new vector
The longest vector is the dominant orientation

14. Description
Split the interest region (20s x 20s) up into 4 x 4 square sub-regions.
Calculate Haar wavelet response dx and dy and weight the response with a Gaussian kernel.
Sum the response over each sub-region for dx and dy, then sum the absolute value of resp- onse.
Normalize the vector into unit length

15. Matching
Fast indexing through the sign of the Laplacian for the underlying interest point
The sign of trace of the Hessian matrix
Trace = Lxx + Lyy

16. Experiments

17. Experiments

18. Experiments

19. Analysis and Conclusion
SURF is faster than SIFT by 3 times, and has recall precision not worse than SIFT.
SURF is good at handling image with blurring or rotation.
SURF is poor at handling image with viewpoint or illumination change.