Transcript SURF: Speeded-Up Robust Features
SURF
: SPEEDED-UP FEATURES ROBUST
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 2
OUTLINE
Introduction Related Works Speed-Up Robust Features
• Detection • Description
Experiments Conclusion 3
Introduction
• Why do we care about feature matching?
Object Recognition Wide baseline matching Tracking
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Types of variance • Illumination • Scale • Rotation • Affine • Perspective
Challenges
We want to find
Repeatability 、 Distinctiveness
features
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OUTLINE
Introduction Related Works Speed-Up Robust Features
• Detection • Description
Experiments Conclusion 6
Related Works
• • •
Harris Corner Detector - Harris 1988 Laplacian of Gaussian - Lindeberg 1998 Difference of Gaussian - Lowe 2004 7
Related Works
• • •
Harris Corner Detector - Harris 1988 Laplacian of Gaussian - Lindeberg 1998 Difference of Gaussian - Lowe 2004
flat edge corner Illumination invariance !!!
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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
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Related Works
• • •
Harris Corner Detector - Harris 1988 Laplacian of Gaussian - Lindeberg 1998 Difference of Gaussian - Lowe 2004
Computational efficiency !
) (
k
1) 2 2 Compare to 26 neighbors Keep the same keypoint in all scale !
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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 .
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OUTLINE
Introduction Related Works Speed-Up Robust Features
• Detection • Description
Experiments Conclusion 12
Detection
Hessian-based interest point localization
• • • L xx (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
D xx
to approximate
L xx
.
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Detection
Scale analysis with constant image size
(DoG)
Approximated second order derivatives with box filters .
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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 with box filters
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Keypoint detection Keypoint description Keypointmatching
Summary
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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
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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 interest point x response y response Haar scale = s r = 6s dx dy
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Description
• • • Split the interest region (20s x 20s) up into 4 x 4 square sub regions.
Calculate Haar wavelet response d x and d y and weight the response with a Gaussian kernel.
Sum the response over each sub-region for d x and d y , then sum the absolute value of resp onse.
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Matching
•
Fast indexing through the sign of the Laplacian underlying interest point for the
The sign of trace of the Hessian matrix
Trace = L xx + L yy
can do match can do match not match matching
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OUTLINE
Introduction Related Works Speed-Up Robust Features
• Detection • Description
Experiments Conclusion 21
Experiments
•
Test keypoint repeatability for (Viewpoint Change), (Lighting Change) and(Zoom and Rotation) 22
Experiments
•
Repeatability score for image sequences 23
Experiments
•
Fix number of keypoints 24
Experiments
SIFT SURF Leila Mirmohamadsadeghi , “Image Tag Propagation “ ‘10
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Experiments
Image size : 341x341 Running time : 2.411188 seconds
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Experiments
Image size : 800x600 Running time : 12.028462 seconds
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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 .
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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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