image warping and stitching - uc santa...
TRANSCRIPT
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Geometric Transformation and
Image Warping
Prof. Xin Yang
HUST
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Definitions
T
Geometric transformation: Given two images, what is the transformation T between corresponding coordinates? Image Warping: Given a source image and a transformation T, what does the transformed output look like?
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Obtain a wider angle view by combining multiple images.
image fro
m S. Seitz
. . .
Motivations: Panaroma
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Time-lapse Mining from Internet Photos
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Motivations: Analyzing Patterns and Shapes
Automatically rectified floor
The floor (enlarged)
What is the shape of the b/w floor pattern?
Slide from Antonio Criminisi
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Motivations: Image Editing
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Global Geometric Transformation
Original Translation Rotation Aspect
Scaling Affine Perspective
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Global Geometric Transformation
• Transformation T is a coordinate-changing machine:
p’ = T(p)
• What does it mean that T is global? – It is the same for any point p
– It can be described by just a few numbers (parameters)
• Let’s represent T as a matrix:
T
p = (x, y) p’ = (x’,y’)
1 2
3 4
'
'
x
y
ta ax x xT
ta ay y y
Source: Alyosha Efros
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1 2
3 4
'
'
x
y
ta ax x xT
ta ay y y
Translation
' 1 0
' 0 1
x
y
tx x
ty y
Rotation
' cos sin
' sin cos
x x
y y
' 0
' 0
x a x
y b y
' 0
' 0
x a x
y a y
Aspect Scaling
Affine
' a cos bsin
' sin bcos
x
y
tx x
ty a y
Perspective
'
' f
1 1 1
x a b c x
y d e y
m n
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How to Estimate Geometric Transformation
1 2
3 4
'
'
x
y
ta ax x xT
ta ay y y
Estimate T is to estimate parameters a1, a2, a3, a4 , tx, ty
Three correspondences can compute a transformation Given a set of correspondences (include both inliers and outliers) How to derive a correct T?
Inliers: correct pairs of matching points which comply with the true transformation Outliers: erroneous pairs of matching points from two images which hurt the quality of parameter estimates
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Outliers affect least squares fit
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Outliers affect least squares fit
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RANSAC for Estimate Geometric Transformation
• RANdom Sample Consensus
• Approach: we want to avoid the impact of outliers, so let’s look for “inliers”, and use those only.
• Intuition: if an outlier is chosen to compute the current fit, then the resulting line won’t have much support from rest of the points.
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RANSAC for line fitting example
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
Least-squares fit
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
1. Randomly select minimal subset of points
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
1. Randomly select minimal subset of points
2. Hypothesize a model
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
1. Randomly select minimal subset of points
2. Hypothesize a model
3. Compute error function
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
1. Randomly select minimal subset of points
2. Hypothesize a model
3. Compute error function
4. Select points consistent with model
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
1. Randomly select minimal subset of points
2. Hypothesize a model
3. Compute error function
4. Select points consistent with model
5. Repeat hypothesize-and-verify loop
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
1. Randomly select minimal subset of points
2. Hypothesize a model
3. Compute error function
4. Select points consistent with model
5. Repeat hypothesize-and-verify loop
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
1. Randomly select minimal subset of points
2. Hypothesize a model
3. Compute error function
4. Select points consistent with model
5. Repeat hypothesize-and-verify loop
Uncontaminated sample
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting example
1. Randomly select minimal subset of points
2. Hypothesize a model
3. Compute error function
4. Select points consistent with model
5. Repeat hypothesize-and-verify loop
Source: R. Raguram Lana Lazebnik
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RANSAC for line fitting
• Repeat N times:
• Draw s points uniformly at random
• Fit line to these s points
• Find inliers to this line among the remaining points (i.e., points whose distance from the line is less than t)
• If there are d or more inliers, accept the line and refit using all inliers
Lana Lazebnik
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• That is an example fitting a model
• (line)…
• What about fitting a transformation (translation)?
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RANSAC example: Translation
Select one match, count inliers
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RANSAC example: Translation
Select one match, count inliers
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RANSAC example: Translation
Find “average” translation vector
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RANSAC for estimating homography
• RANSAC loop:
• 1. Select four feature pairs (at random)
• 2. Compute homography H (exact)
• 3. Compute inliers where SSD(pi’, Hpi)< ε
• 4. Keep largest set of inliers
• 5. Re-compute least-squares H estimate on all of the inliers
Slide credit: Steve Seitz
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RANSAC pros and cons
• Pros – Simple and general
– Applicable to many different problems
– Often works well in practice
• Cons – Lots of parameters to tune
– Doesn’t work well for low inlier ratios (too many iterations, or can fail completely)
– Can’t always get a good initialization of the model based on the minimum number of samples
Lana Lazebnik
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Recent Work for Geometric Transformation based on Correspondences
• O. Chum et al. Matching with PROSAC – Progressive Sample Consensus. In Proc. of CVPR, 2005
• C. Sweeney et al. gDLS: A Scalable Solution to the Generalized Pose and Scale Problem. In Proc. of ECCV, 2014
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Mosaics
Obtain a wider angle view by combining multiple images.
image fro
m S. Seitz
. . .
T1
T2 T3
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Image warping
Given a coordinate transform T and a source image f(x,y),
how do we compute a transformed image g(x’,y’) = f(T(x,y))?
x x’
T(x,y)
f(x,y) g(x’,y’)
y y’
Slide from Alyosha Efros, CMU
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Image warping
• Send each pixel f(x,y) to its corresponding location (x’,y’) = T(x,y) in the second image
Q: what if pixel lands “between” two pixels?
f(x,y) g(x’,y’) x x’
T(x,y)
y y’
Slide from Alyosha Efros, CMU
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Image warping
• Send each pixel f(x,y) to its corresponding location (x’,y’) = T(x,y) in the second image
Q: what if pixel lands “between” two pixels?
f(x,y) g(x’,y’) x x’
T(x,y)
y y’
A: Interpolate color value from neighbors – nearest neighbor, bilinear…
Slide from Alyosha Efros, CMU
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Bilinear interpolation
Sampling at f(x,y):
Slide from Alyosha Efros, CMU
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Nearest Neighbor (3X)
HQ 3Xhttp://en.wikipedia.org/wiki/Image_scaling
HQ (3X)
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Recap: How to stitch together a panorama (a.k.a. mosaic)?
• Basic Procedure
– Take a sequence of images from the same position • Rotate the camera about its optical center
– Compute transformation (homography) between second image and first using corresponding points.
– Transform the second image to overlap with the first.
– Blend the two together to create a mosaic.
– (If there are more images, repeat)
Source: Steve Seitz
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Robust feature-based alignment
Source: L. Lazebnik
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Robust feature-based alignment
• Extract features
Source: L. Lazebnik
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Robust feature-based alignment
• Extract features • Compute putative matches
Source: L. Lazebnik
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Robust feature-based alignment
• Extract features • Compute putative matches • Loop:
– Hypothesize transformation T (small group of putative matches that are related by T)
Source: L. Lazebnik
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Robust feature-based alignment
• Extract features • Compute putative matches • Loop:
– Hypothesize transformation T (small group of putative matches that are related by T)
– Verify transformation (search for other matches consistent with T)
Source: L. Lazebnik
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Robust feature-based alignment
• Extract features
• Compute putative matches
• Loop: – Hypothesize transformation T (small group of putative
matches that are related by T)
– Verify transformation (search for other matches consistent with T) Source: L. Lazebnik
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Creating and Exploring a Large Photorealistic Virtual Space
Josef Sivic, Biliana Kaneva, Antonio Torralba, Shai Avidan and William T. Freeman, Internet Vision Workshop, CVPR 2008. http://www.youtube.com/watch?v=E0rboU10rPo
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Summary: transformation & warping
• Write 2d transformations as matrix-vector multiplication
• Estimating geometric transformations: solve for unknown parameters given corresponding points from two views (exemplar method: RANSAC).
• Mosaics: uses homography and image warping to merge views taken from same center of projection