Lecture 12 - Stanford Vision Lab

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Overview: SIFT• Extraordinarily robust matching technique– Can handle changes in viewpoint up to ~60 deg. out‐of‐plane rotation– Can handle significant changes in illumination• Sometimes even day vs. night (below)– Fast and efficient—can run in real time– Lots of code available• http://people.csail.mit.edu/albert/ladypack/wiki/index.php/Known_implementations_of_SIFTSlide credit: Steve SeitzFei-Fei LiLecture 12 -185‐Nov‐11
Working with SIFT Descriptors• One image yields:– n 128‐dimensional descriptors: eachone is a histogram of the gradientorientations within a patch• [n x 128 matrix]– n scale parameters specifying thesize of each patch• [n x 1 vector]– n orientation parameters specifyingthe angle of the patch• [n x 1 vector]– n 2D points giving positions of thepatches• [n x 2 matrix]Slide credit: Steve SeitzFei-Fei LiLecture 12 -195‐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: Feature Descriptors: SIFT• Scale
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 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 70 and 71:
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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Uses: Analyzing Patterns and Shapes
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Lecture 12 - Stanford Vision Lab

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