SURF: Speeded-Up Robust Features Advisor : Sheng-Jyh Wang Student : 劉彥廷 2011/10/17 Computer Vision and Image Understanding (CVIU) 2008.

Outline Introduction Related Works Speed-Up Robust Features Detection Description Experiments Conclusion

Outline Introduction Related Works Speed-Up Robust Features Detection Description Experiments Conclusion

Introduction Why do we care about feature matching? Object Recognition Wide baseline matching Tracking

Challenges Types of variance Illumination Scale Rotation Affine Perspective We want to find Repeatability、Distinctiveness features

Outline Introduction Related Works Speed-Up Robust Features Detection Description Experiments Conclusion

Related Works Harris Corner Detector - Harris 1988 Laplacian of Gaussian - Lindeberg 1998 Difference of Gaussian - Lowe 2004

Related Works flat edge corner Illumination invariance !!! Harris Corner Detector - Harris 1988 Laplacian of Gaussian - Lindeberg 1998 Difference of Gaussian - Lowe 2004 flat edge corner Illumination invariance !!!

* Related Works = Harris Corner Detector - Harris 1988 Laplacian of Gaussian - Lindeberg 1998 Difference of Gaussian - Lowe 2004 characteristic scale * = LoG can detect blob-like structures at locations “Feature Detection with Automatic Scale Selection”, IJCV ‘98

Related Works Harris Corner Detector - Harris 1988 Laplacian of Gaussian - Lindeberg 1998 Difference of Gaussian - Lowe 2004 Computational efficiency ! Compare to 26 neighbors Keep the same keypoint in all scale !

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

Outline Introduction Related Works Speed-Up Robust Features Detection Description Experiments Conclusion

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. This paper use Dxx to approximate Lxx.

Detection Scale analysis with constant image size Approximated second order derivatives with box filters. (DoG) 但是拿Gaussian derivatives來做，需要對Gaussian Function做離散化和裁切 比較浪費時間，尤其是filter大小又需要改變的時候    

Integral Images 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. They can be evaluated at a very low computational cost using integral images ABCD所圍面積內intensity的總和 = A-B-C+D They can be evaluated at a very low computational cost using integral images with box filters

Summary         Keypoint detection Keypoint description Keypointmatching

Fourier v.s. Wavelet Fourier Transform (FT) is not a good tool – gives no direct information about when an oscillation occurred. Wavelets can keep track of time and frequency information. Fourier basis Haar basis

Description interest point Haar dx scale = s r = 6s dy Orientation Assignment The Haar wavelet responses are represented as vectors Sum all responses within a sliding orientation window covering an angle of 60 degree The longest vector is the dominant orientation x response y response dx dy r = 6s interest point scale = s Haar

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.

Matching  can do match  can do match  not match 20 Fast indexing through the sign of the Laplacian for the underlying interest point The sign of trace of the Hessian matrix Trace = Lxx + Lyy  can do match  can do match  not match matching 20

Outline Introduction Related Works Speed-Up Robust Features Detection Description Experiments Conclusion

Experiments Test keypoint repeatability for (Viewpoint Change), (Lighting Change) and(Zoom and Rotation)

Experiments Repeatability score for image sequences

Experiments Fix number of keypoints

Experiments SIFT SURF Leila Mirmohamadsadeghi , “Image Tag Propagation “ ‘10

Experiments Image size : 341x341 Running time : 2.411188 seconds

Experiments Image size : 800x600 Running time : 12.028462 seconds

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 .

Reference “Speeded-Up Robust Features”, CVIU ‘08 Herbert Bay “Distinctive Image Features from Scale-Invariant Features”, IJCV ’04 David G. Lowe “A Combined Corner and Edge Detector” ‘88 Chris Harris “Feature Detection with Automatic Scale Selection”, IJCV ’98 Lindeberg