Image Stitching Tamara Berg CSE 590 Computational Photography Many slides from Alyosha Efros & Derek Hoiem

How can we align two pictures? What about global matching?

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 B1B1 B2B2 B3B3 A1A1 A2A2 A3A3 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

Question Why not just take every patch in the original image and find best match in second image?

Goals for Keypoints Detect points that are repeatable and distinctive

Key trade-offs More Points More Repeatable B1B1 B2B2 B3B3 A1A1 A2A2 A3A3 Localization More Robust More Selective Description Robust to occlusion Works with less texture Robust detection Precise localization Deal with expected variations Maximize correct matches Minimize wrong matches

Keypoint Localization Goals: –Repeatable detection –Precise localization K. Grauman, B. Leibe

Which patches are easier to match? ?

Choosing interest points If you wanted to meet a friend would you say a)“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.

Harris Detector – Responses [ Harris88 ] Effect: A very precise corner detector.

Harris Detector – Responses [ Harris88 ]

So far: can localize in x-y, but not scale

Automatic Scale Selection K. Grauman, B. Leibe How to find corresponding patch sizes?

Automatic Scale Selection Function responses for increasing scale (scale signature) K. Grauman, B. Leibe

Automatic Scale Selection K. Grauman, B. Leibe Function responses for increasing scale (scale signature)

Automatic Scale Selection K. Grauman, B. Leibe Function responses for increasing scale (scale signature)

Automatic Scale Selection K. Grauman, B. Leibe Function responses for increasing scale (scale signature)

Automatic Scale Selection K. Grauman, B. Leibe Function responses for increasing scale (scale signature)

Automatic Scale Selection K. Grauman, B. Leibe 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  Original image Sampling with step    =2   

Results: Lowe’s DoG K. Grauman, B. Leibe

T. Tuytelaars, B. Leibe Orientation Normalization Compute orientation histogram Select dominant orientation Normalize: rotate to fixed orientation 0 2  [Lowe, SIFT, 1999]

Available at a web site near you… For most local feature detectors, executables are available online: – – – K. Grauman, B. Leibe

How do we describe the keypoint?

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 VaishVaibhav 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 Nikon D70s, Tokina 16mm, f/22, 1/40s

Photo: Russell J. Hewett Pike’s Peak Highway, CO (See Photo On Web)See Photo On Web

Photo: Russell J. Hewett 360 Degrees, Tripod Leveled Nikon D70, Tokina 12mm, f/8, 1/125s

Photo: Russell J. Hewett Howth, Ireland (See Photo On Web)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 Handheld Camera Nikon D70s, Nikon 70mm, f/6.3, 1/200s

Photo: Russell J. Hewett Handheld Camera

Photo: Russell J. Hewett Les Diablerets, Switzerland (See Photo On Web)See Photo On Web

Photo: Russell J. Hewett & Bowen Lee Macro Nikon D70s, Tamron 90mm 90mm, f/10, 15s

Photo: Russell J. Hewett & Bowen Lee Side of Laptop

Photo: Russell J. Hewett Ghosting and Variable Intensity Nikon D70s, Tokina 12mm, f/8, 1/400s

Photo: Russell J. Hewett

Photo: Bowen Lee Ghosting From Motion Nikon e4100 P&S

Photo: Russell J. Hewett Nikon D70, Nikon 135mm, f/11, 1/320s Motion Between Frames

Photo: Russell J. Hewett

Gibson City, IL (See Photo On Web)See Photo On Web

Photo: Russell J. Hewett Mount Blanca, CO Nikon D70s, Tokina 12mm, f/22, 1/50s

Photo: Russell J. Hewett Mount Blanca, CO (See Photo On Web)See Photo On Web

Image Stitching Algorithm Overview 1.Detect keypoints 2.Match keypoints 3.Estimate homography with matched keypoints (using RANSAC) 4.Project onto a surface and blend

Image Stitching Algorithm Overview 1.Detect keypoints (e.g., SIFT) 2.Match keypoints

Computing homography If we have 4 matched points we can compute homography H

Computing homography Assume we have matched points with outliers: How do we compute homography H? Automatic Homography Estimation with RANSAC

RANSAC: RANdom SAmple Consensus Scenario: 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

RANSAC for Homography Initial Detected Points

RANSAC for Homography Final Matched Points

RANSAC for Homography

Blending to remove artifacts Burt & Adelson 1983

Further reading Harley and Zisserman: Multi-view Geometry book DLT algorithm: HZ p. 91 (alg 4.2), p. 585 Normalization: HZ p (alg 4.2) RANSAC: HZ Sec 4.7, p. 123, alg 4.6 Tutorial: -tutorial/tut_4up.pdf -tutorial/tut_4up.pdf Recognising Panoramas: Brown and Lowe, IJCV 2007 Recognising Panoramas