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Distinctive Image Features from
Scale-Invariant Keypoints
David G. Lowe
International Journal of Computer Vision ,Volume 60 Issue 2,
Pages 91 – 110, 2004.
Presenter :JIA-HONG,DONG
Advisor : Yen- Ting, Chen
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Outline
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Introduction
Methodology
Recognition examples
Simulation and testing
Conclusion
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Introduction
 Scale Invariant Feature Transform(SIFT)
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Object and scene recognition
Video Tracking
Robotic mapping and navigation
Image stitching
3D modeling
Gesture recognition
Match moving
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Introduction
 Advantage
 The features are invariant:
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Image scaling
Image rotation
Illumination
Noise
Camera viewpoint
 High performance:
 High accuracy
 Near real-time
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Introduction
 Major stages of computation:
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Scale-space extrema detection
Keypoint localization
Orientation assignment
Keypoint descriptor
Matching features(nearest neighbor)
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Methodology
 Detection of scale-space extrema
.....(1)
 Convolution
.....(2)
 image subtraction
.....(3)
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Methodology
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Methodology
.....(4)
.....(5)
.....(6)
(К-1) is a constant
factor σ2 is scale invariant as studied by Lindeberg (1994)
σ2Δ2G is maxima and minima image features as studied by Mikolajczyk(2002)
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Methodology
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Methodology
 Database
 32 real images:
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Outdoor scenes
Human faces
Aerial photographs
Industrial images
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Methodology
 Database
 Transformations:
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Rotation
Scaling
Brightness
Contrast
Noise
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Methodology
 Local extrema detection
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Methodology
 Local extrema detection
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Methodology
 Frequency of sampling in scale
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Methodology
 Accurate keypoint localization
.....(3)
 Using Taylor expansion
.....(8)
 x=(x, y , σ )T is the offset from this point
.....(9)
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Methodology
.....(10)
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Methodology
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Methodology
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Methodology
 Orientation assignment
...(11)
.....(12)
L(x,y) is the sample image.
Θ(x,y) is orientation
m(x,y) is the gradient magnitude
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Methodology
 Orientation histogram
 A region around the keypoint
 36 bins covering the 360 degree range of
orientations
 Added weighted
 A Gaussian window σ=1.5
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Methodology
 The local image descriptor
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Methodology
 Descriptor testing
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Methodology
 Descriptor representation
 4x4 array of histograms
 8 orientation bins
 4x4x8 = 128 element feature vector
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Methodology
 Keypoint matching
 Minimum Euclidean distance
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Methodology
 Keypoint matching
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Methodology
 Application to object recognition
 Need 3 features at least
 Higher probability
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Recognition examples
 Descriptor testing
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Recognition examples
 Sensitivity to affine change
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Recognition examples
 Matching to large databases
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Recognition examples
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Recognition examples
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Simulation and testing
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Simulation and testing
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Conclusion
 SIFT keypoints described are
particularly useful.
 A high-dimensional vector represents
the image gradients within a local
region of the image.
 Near real-time performance on
standard PC hardware.
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Conclusion
 Systematic testing is needed on data
sets with full 3D viewpoint.
 Feature sets are likely to contain both
prior and learned features.
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Thank you for your attention
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