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SURF : Speeded-Up Robust Features

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

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SURF : Speeded-Up Robust Features

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

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

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

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

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

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

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

  8. Related Works • Harris Corner Detector - Harris 1988 • Laplacian of Gaussian - Lindeberg 1998 • Difference of Gaussian - Lowe 2004 corner flat edge Illuminationinvariance !!!

  9. 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

  10. 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 !

  11. 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.

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

  13. 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 approximateLxx.

  14. Detection Scale analysis with constant image size Approximated second order derivatives with box filters. (DoG)

  15. Integral Images • Using integral images for major speed up • Integral Image (summed area tables) is an intermediate representation for the image and contains thesum of gray scale pixel values of image. They can be evaluated at a very low computational cost using integral images with box filters

  16. Summary • Keypoint detection • Keypoint description • Keypointmatching

  17. 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

  18. Description 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 Haar interest point x response y response dx scale = s r = 6s dy

  19. Description • Split the interest region(20s x 20s) up into 4 x 4 square sub-regions. • CalculateHaar waveletresponse dx and dyand weight the response with a Gaussian kernel. • Sum the response over each sub-region for dxand dy, then sum the absolute value of resp-onse.

  20. 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  can do match  can do match  not match matching 20

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

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

  23. Experiments • Repeatability score for image sequences

  24. Experiments • Fix number of keypoints

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

  26. Experiments Image size : 341x341 Running time : 2.411188 seconds

  27. Experiments Image size : 800x600 Running time : 12.028462 seconds

  28. Conclusion • SURF isfaster 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 athandling image with viewpoint .

  29. 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

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