Lecture 12 - Stanford Vision Lab

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3D Object Recognition• Gen. Hough T. for Recognition[Lowe99]– Typically only 3 feature matchesneeded for recognition– Extra matches providerobustness– Affine model can be used forplanar objectsFei-Fei LiLecture 12 -345‐Nov‐11
View Interpolation• Training– Training views from similarviewpoints are clusteredbased on feature matches.– Matching features betweenadjacent views are linked.• Recognition– Feature matches may bespread over severaltraining viewpoints. Use the known links to “transfer votes” to otherviewpoints.Fei-Fei LiLecture 12 -35 5‐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 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 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 56 and 57: Probabilistic model for verificatio
Page 62 and 63: Error function• Sum of squared pr
Page 64 and 65: Bundle Adjustment• New images ini
Page 66 and 67: Multi‐band Blending• Burt & Ade
Page 68 and 69: Linear BlendingFei-Fei LiLecture 12
Page 72 and 73: Applications of Local Invariant Fea
Page 74 and 75: Recognition of Specific Objects, Sc
Page 76 and 77: References and Further Reading• M
Page 78 and 79: Supplementary materials• Matching
Page 80 and 81: Alignment Problem• We have previo
Page 82 and 83: What Can be Represented by a 22 Mat
Page 84 and 85:
2D Linear Transformsx ' a bxy' c
Page 86 and 87:
2D Affine Transformationx' ay' d
Page 88 and 89:
Fitting an Affine Transformation•
Page 90 and 91:
Fitting a Projective Transformation
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Uses: Analyzing Patterns and Shapes
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Lecture 12 - Stanford Vision Lab

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