Lecture 12 - Stanford Vision Lab

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Iterative Affine Adaptation1. Detect keypoints, e.g. multi‐scale Harris2. Automatically select the scales3. Adapt affine shape based on second order moment matrix4. Refine point locationK. Mikolajczyk and C. Schmid, Scale and affine invariant interest point detectors, IJCV 60(1):63‐86, 2004.Slide credit: Tinne TuytelaarsFei-Fei LiLecture 12 -85‐Nov‐11
Affine Normalization/Deskewingrotaterescale• Steps– Rotate the ellipse’s main axis to horizontal– Scale the x axis, such that it forms a circleSlide credit: Tinne TuytelaarsFei-Fei LiLecture 12 -95‐Nov‐11
Page 1 and 2: Lecture 12:Local Descriptors, SIFT&
Page 3: Local Descriptors• We know how to
Page 6 and 7: The Need for Invariance• Up to no
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 48 and 49: Why “Recognising Panoramas”?Sli
Page 50 and 51: Nearest Neighbor Matching of SIFT F
Page 52 and 53: Slide credit: Matthew BrownFei-Fei
Page 54 and 55: Slide credit: Matthew BrownFei-Fei
Page 56 and 57: Probabilistic model for verificatio
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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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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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Uses: Analyzing Patterns and Shapes
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Lecture 12 - Stanford Vision Lab

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