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CPSC 643, Presentation 2. SURF : Speeded-Up Robust Features. Herbert Bay a , Andreas Ess a , Tinne Tuytellares b , Luc Van Gool a,b a ETH Zurich b K. U. Leuven, ESAT-PSI Sternwartstrasse 7 Kasteel Arenberg 10

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SURF : Speeded-Up Robust Features


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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 7 Kasteel Arenberg 10

CH-8092 Zurich B-3001 Leuven

Switzerland Belgium

Computer Vision and Image Understanding (CVIU)

Vol.110, No.3, pp. 346-359, 2008.

slide2

Mostly Related Works

  • Harris Corner Detector - Harris 1988
  • Laplacian of Gaussian - Lindeberg 1998
  • Harris - Laplace Detector- Mikolajczyk 2001
  • Difference of Gaussian - Lowe 2004
slide3

Mostly Related Works

  • Harris Corner Detector - Harris 1988
  • Laplacian of Gaussian - Lindeberg 1998
  • Harris - Laplace Detector- Mikolajczyk 2001
  • Difference of Gaussian - Lowe 2004
slide4

Mostly Related Works

  • Harris Corner Detector - Harris 1988
  • Laplacian of Gaussian - Lindeberg 1998
  • Harris - Laplace Detector- Mikolajczyk 2001
  • Difference of Gaussian - Lowe 2004
slide5

Mostly Related Works

  • Harris Corner Detector - Harris 1988
  • Laplacian of Gaussian - Lindeberg 1998
  • Harris - Laplace Detector - Mikolajczyk 2001
  • Difference of Gaussian - Lowe 2004
slide6

Mostly Related Works

  • Harris Corner Detector - Harris 1988
  • Laplacian of Gaussian - Lindeberg 1998
  • Harris - Laplace Detector - Mikolajczyk 2001
  • Difference of Gaussian- Lowe 2004
slide7

Other Related Works

  • Salient Region Detector - Kadir 2001
  • Edge-based Region Detector - Jurie 2004
slide8

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.
slide9

Methodology

  • 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.
slide10

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 approximateLxx.
slide11

Detection

Approximated second order derivatives with box filters.

Scale analysis with constant image size

slide12

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)

slide13

Description

  • Dominant orientation
  • The Haar wavelet responses are represented as vectors
  • Sum all responses withina sliding orientationwindow covering an angle of 60 degree
  • The two summed response yield a new vector
  • The longest vector is the dominant orientation
slide14

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.
  • Normalize the vector into unit length
slide15

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

slide19

Analysis and 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 or illumination change.