- Page 1: Lecture 12:Local Descriptors, SIFT&
- Page 6 and 7: The Need for Invariance• Up to no
- Page 8 and 9: Iterative Affine Adaptation1. Detec
- Page 10 and 11: Affine Adaptation ExampleScale‐in
- Page 12: Summary: Affine‐Inv. Feature Extr
- Page 17 and 18: Feature Descriptors: SIFT• Scale
- Page 19: Working with SIFT Descriptors• On
- Page 22 and 23: Other local descriptors:Steerable f
- Page 24 and 25: Other local descriptors:GLOHFei-Fei
- Page 26 and 27: Other local descriptors:Geometric B
- Page 28 and 29: http://www.robots.ox.ac.uk/~vgg/res
- Page 30 and 31: What we will learn today• Local d
- Page 32 and 33: Finding Consistent Configurations
- Page 34 and 35: 3D Object Recognition• Gen. Hough
- Page 36 and 37: Recognition Using View Interpolatio
- Page 38 and 39: What we will learn today• Local d
- Page 40 and 41: Introduction• Are you getting the
- Page 42 and 43: Why “Recognising Panoramas”?•
- Page 44 and 45: Why “Recognising Panoramas”?•
- Page 46 and 47: Why “Recognising Panoramas”?•
- Page 48 and 49: Why “Recognising Panoramas”?Sli
- Page 50 and 51: Nearest Neighbor Matching of SIFT F
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Slide credit: Matthew BrownFei-Fei
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Slide credit: Matthew BrownFei-Fei
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Probabilistic model for verificatio
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Slide credit: Matthew BrownFei-Fei
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Slide credit: Matthew BrownFei-Fei
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Error function• Sum of squared pr
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Bundle Adjustment• New images ini
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Multi‐band Blending• Burt & Ade
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Linear BlendingFei-Fei LiLecture 12
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Slide credit: Matthew BrownFei-Fei
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Applications of Local Invariant Fea
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Recognition of Specific Objects, Sc
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References and Further Reading• M
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Supplementary materials• Matching
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Alignment Problem• We have previo
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What Can be Represented by a 22 Mat
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2D Linear Transformsx ' a bxy' c
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2D Affine Transformationx' ay' d
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Fitting an Affine Transformation•
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Fitting a Projective Transformation
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Fitting a Projective Transformation
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Fitting a Projective Transformation
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Fitting a Projective Transformation
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Fitting a Projective Transformation
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Uses: Analyzing Patterns and Shapes