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Image Stitching
Tamara BergCSE 590 Computational Photography
Many slides from Alyosha Efros & Derek Hoiem
How can we align two pictures?
• Global matching?– But what if
• Not just translation change, but rotation and scale?• Only small pieces of the pictures match?
Keypoint Matching
K. Grauman, B. Leibe
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1. Find a set of distinctive key- points
3. Extract and normalize the region content
2. Define a region around each keypoint
4. Compute a local descriptor from the normalized region
5. Match local descriptors
Main challenges
• Change in position, scale, and rotation
• Change in viewpoint
• Occlusion
• Articulation, change in appearance
Key trade-offs
More Points More Repeatable
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B2
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Localization
More Robust More Selective
Description
Robust to occlusionWorks with less texture
Robust detectionPrecise localization
Deal with expected variationsMaximize correct matches
Minimize wrong matches
Choosing interest points
• If you wanted to meet a friend would you saya) “Let’s meet on campus.”b) “Let’s meet on Green street.”c) “Let’s meet at Green and Wright.”
• Or if you were in a secluded area:a) “Let’s meet in the Plains of Akbar.”b) “Let’s meet on the side of Mt. Doom.”c) “Let’s meet on top of Mt. Doom.”
Choosing interest points
• Corners– “Let’s meet at Green and Wright.”
• Peaks/Valleys – “Let’s meet on top of Mt. Doom.”
Many Existing Detectors Available
K. Grauman, B. Leibe
Hessian & Harris [Beaudet ‘78], [Harris ‘88]Laplacian, DoG [Lindeberg ‘98], [Lowe 1999]Harris-/Hessian-Laplace [Mikolajczyk & Schmid ‘01]Harris-/Hessian-Affine [Mikolajczyk & Schmid ‘04]EBR and IBR [Tuytelaars & Van Gool ‘04] MSER [Matas ‘02]Salient Regions [Kadir & Brady ‘01] Others…
Harris Detector [Harris88]
K. Grauman, B. Leibe
Intuition: Search for local neighborhoods where the image content has two main directions.
Automatic Scale Selection
K. Grauman, B. Leibe
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How to find corresponding patch sizes?
Automatic Scale Selection• Function responses for increasing scale (scale signature)
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Automatic Scale Selection
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• Function responses for increasing scale (scale signature)
Automatic Scale Selection
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• Function responses for increasing scale (scale signature)
Automatic Scale Selection
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• Function responses for increasing scale (scale signature)
Automatic Scale Selection
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• Function responses for increasing scale (scale signature)
Automatic Scale Selection
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• Function responses for increasing scale (scale signature)
What Is A Useful Signature Function?
• Difference of Gaussian = “blob” detector
K. Grauman, B. Leibe
DoG – Efficient Computation• Computation in Gaussian scale pyramid
K. Grauman, B. Leibe
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T. Tuytelaars, B. Leibe
Orientation Normalization
• Compute orientation histogram• Select dominant orientation• Normalize: rotate to fixed orientation
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[Lowe, SIFT, 1999]
Available at a web site near you…
• For most local feature detectors, executables are available online:– http://robots.ox.ac.uk/~vgg/research/affine– http://www.cs.ubc.ca/~lowe/keypoints/– http://www.vision.ee.ethz.ch/~surf
K. Grauman, B. Leibe
Local Descriptors
• The ideal descriptor should be– Robust– Distinctive– Compact– Efficient
• Most available descriptors focus on edge/gradient information– Capture texture information– Color rarely used
K. Grauman, B. Leibe
Local Descriptors: SIFT Descriptor
[Lowe, ICCV 1999]
Histogram of oriented gradients
• Captures important texture information
• Robust to small translations / affine deformations
K. Grauman, B. Leibe
What to use when?
Detectors• Harris gives very precise localization but doesn’t
predict scale– Good for some tracking applications
• DOG (difference of Gaussian) provides ok localization and scale– Good for multi-scale or long-range matching
Descriptors• SIFT: good general purpose descriptor
Things to remember• Keypoint detection: repeatable
and distinctive– Corners, blobs– Harris, DoG
• Descriptors: robust and selective– SIFT: spatial histograms of gradient
orientation
Image Stitching• Combine two or more overlapping images to
make one larger image
Add example
Slide credit: Vaibhav Vaish
Panoramic Imaging
• Higher resolution photographs, stitched from multiple images
• Capture scenes that cannot be captured in one frame
• Cheaply and easily achieve effects that used to cost a lot of money
Photo: Russell J. Hewett
Pike’s Peak Highway, CO
(See Photo On Web)
Photo: Russell J. Hewett
Howth, Ireland
(See Photo On Web)
Capturing Panoramic Images
• Tripod vs Handheld• Help from modern cameras• Leveling tripod• Or wing it
• Exposure• Consistent exposure between frames• Gives smooth transitions• Manual exposure
• Caution• Distortion in lens (Pin Cushion, Barrel, and Fisheye)• Motion in scene
• Image Sequence• Requires a reasonable amount of overlap (at least 15-30%)• Enough to overcome lens distortion
Photo: Russell J. Hewett
Les Diablerets, Switzerland
(See Photo On Web)
Photo: Russell J. Hewett
Ghosting and Variable Intensity
Nikon D70s, Tokina 12-24mm @ 12mm, f/8, 1/400s
Photo: Russell J. Hewett
Gibson City, IL
(See Photo On Web)
Photo: Russell J. Hewett
Mount Blanca, CO
(See Photo On Web)
Image Stitching Algorithm Overview
1. Detect keypoints2. Match keypoints3. Estimate homography with matched
keypoints (using RANSAC)4. Project onto a surface and blend
Computing homography
If we have 4 matched points we can compute homography H
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Computing homography
Assume we have matched points with outliers: How do we compute homography H?
Automatic Homography Estimation with RANSAC
RANSAC: RANdom SAmple ConsensusScenario: We’ve got way more matched points than needed to fit the parameters, but we’re not sure which are correct
RANSAC Algorithm• Repeat N times
1. Randomly select a sample– Select just enough points to recover the parameters (4)2. Fit the model with random sample
3. See how many other points agree• Best estimate is one with most agreement
– can use agreeing points to refine estimate
Automatic Image Stitching
1. Compute interest points on each image
2. Find candidate matches
3. Estimate homography H using matched points and RANSAC
4. Project each image onto the same surface and blend
Further reading
Harley and Zisserman: Multi-view Geometry book• DLT algorithm: HZ p. 91 (alg 4.2), p. 585• Normalization: HZ p. 107-109 (alg 4.2)• RANSAC: HZ Sec 4.7, p. 123, alg 4.6• Tutorial:
http://users.cecs.anu.edu.au/~hartley/Papers/CVPR99-tutorial/tut_4up.pdf
• Recognising Panoramas: Brown and Lowe, IJCV 2007