Lecture 12 - Stanford Vision Lab

More documents

Recommendations

Info

Nearest Neighbor Matching of SIFT Features0 2 • Find k‐NN for each feature– k number of overlapping images (we use k = 4)• Use k‐d tree– k‐d tree recursively bi‐partitions data at mean in thedimension of maximum variance– Approximate nearest neighbors found in O(nlogn)Slide credit: Matthew BrownFei-Fei LiLecture 12 -505‐Nov‐11
Algorithm (Brown & Lowe, 2003)1. Feature Matching2. Image Matchinga) RANSAC for Homographyb) Probabilistic model for verification3. Bundle Adjustment4. Multi‐band BlendingFei-Fei LiLecture 12 -515‐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 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 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 100 and 101:
Uses: Analyzing Patterns and Shapes
show all

Lecture 12 - Stanford Vision Lab

Create successful ePaper yourself

Delete template?

Save as template?