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COMP37212 - Comp Vis > Week 8 - Image stitching > Flashcards

Week 8 - Image stitching Flashcards

1
Q

What is Panorama stitching

A

An application of SIFT
taking different images with different rotations and stitch them together

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2
Q

What is the issue with just directly stitching two images

A

There will be illumination changes eg dark corners that will show up

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3
Q

What is the first step of image stitching

A

Finding corresponding points

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4
Q

What is image alignment

A

Fitting a model M to a transformation between pairs of features (matches) in two images
Find transformation T that minimises
Σresidual (T(xi),x’i)

residual = residual errors
xi is the set of points

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5
Q

2D transformation models: Euclidean

A

translation + rotation

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6
Q

2D transformation models: Affine

A

translation + rotation + scaling + shear

(shear = stretching
eg rectangle to rhombus)

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7
Q

2D transformation models: Similarity

A

translation + rotation + scaling

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8
Q

2D transformation models: projective

A

homography
includes out of plane rotations

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9
Q

what is x,y in a grayscale function

A

the domain

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10
Q

what is the pixel intensity I in the grayscale function

A

the range

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11
Q

What does filtering/convolution do in terms of pixels

A

changes the pixel values (range)
G(x) = h{F(x)}

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12
Q

What does warping do in terms of pixels

A

changes the pixel locations (domain)
G(x) = F(h{x})

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13
Q

What are examples of global warping/transformation

A

translation
rotation
scaling and aspect
affine
perspective
barrel

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14
Q

What is transformation T

A

A coordinate changing machine
p’ = T(p)

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15
Q

What does it mean when T is global

A

is the same for any point p (applied to all)
can be described by just a few numbers (parameters)

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16
Q

What is uniform scaling

A

same scaling for each components
S = 2x2 = [sx 0 0 sy]

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17
Q

What is Rotation matrix around θ

A

2x2 = [cosθ -sinθ sinθ cosθ]

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18
Q

How do we represent translation in matrix form

A

Not possible
translation is not a linear operation on 2D coordinates

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19
Q

What are all 2D linear transformations a combination of (4 types)

A

scale
rotation
shear
mirror

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20
Q

what are the properties of linear transformations

A

■ Origin maps to origin
■ Lines map to lines
■ Parallel lines remain parallel
■ Ratios are preserved
■ Closed under composition

21
Q

What is Closed under composition

A

apply multiple linear transformations successively by multiplying the transformation matrices then applying

22
Q

What are homogeneous coordinates

A

represent 2d point with a 3d vector up to a defined scale
move from 2d to 3d
(x,y,w)

23
Q

how to convert back from homogeneous coordinates

A

(x, y,w) -> (x/w, y/w)

24
Q

What are basic affine transformations

A

scale
shear
translation
2d in-plane rotation

25
What is always the matrix form for basic affine transformations
any 3x3 matrix with bottom row: 0 0 1
26
what is the only difference between properties of affine and linear transformations
affine transformations do not necessarily map to origin
27
What are all Affine transformations a combination of
linear transformations and translations
28
If we stay in same place and rotate camera is it an affine transformations
No
29
What happens if the bottom row of 3x3 transformation matrix is not 001
Plane projective transformations or Homographies Projects one plane onto a different plane via a particular point of projection
30
What are black areas in homographies
Where there is no pixel available from original image to map new perspective projection
31
What are homographies a combination of
affine transformations and projective warps
32
what is the difference between linear transformations and homographies
origin does not necessarily map to origin parallel lines do not necessarily remain parallel ratios are not preserved (still: Lines map to lines, Closed under composition)
33
How do we solve homographies
Defines a constrained least squares problem
34
What do we assume about the length of vector when solving homographies
lenght of vector (h00...h22) is 1 (we normalise it) so we only have 8 unknowns Need 4 pairs of (x,y) to solve
35
What do we generally assume about feature extraction
That we have extracted a set of correct correspondences between the two images (ground truths) generally, this is not the case
36
Image alignment: outliers
(incorrect match) image alignment does not work well with outliers least squares does not find a good line of best fit with outliers
37
What are inliers
correct matches
38
How do we deal with outliers
- Given a hypothesized line - Count the number of points that “agree” with the line - they are on the line ■ “Agree” = within a small distance of the line ■ I.e., the inliers to that line - For all possible lines, select the one with the largest number of inliers
39
What is RANSAC
(RANdom SAmple Consensus)
40
How does RANSAC work
RANSAC loop: 1. Randomly select a s sample matches s == minimum sample size that lets you fit a transformation model 2. Compute transformation (find a model that fits) from sample group 3. Find inliers to this transformation 4. If the number of inliers is sufficiently large (model is good fit), re-compute least squares estimate of transformation on all of the inliers This refine the inliers to improve accuracy 5. Repeat N times 6. Keep the transformation with the largest n (needs amount inliers to be larger that outliers to work)
41
How many times do we runs RANSAC
related to how many outliers we expect and probability of success we would like
42
What is forward warping
we have source and target image and T homography matrix send each pixel x,y to its corresponding x',y' = T(x,y)
43
What is the issue of forward warping
if a pixel lands between two pixels solve by adding a contribution normalise later (splitting) But we can still get holes
44
What is Inverse warping
Take each pixel from target image and find inverse transform move from x'y' to x'y' using x,y = T-1(x',y')
45
What is the issue with inverse warping
if the pixel again comes between two pixels we can resample colour values from interpolated source image (we have all the information)
46
What are the steps of creating panoramas
detect features SIFT match features compute a homography using RANSAC combine images together (image blending)
47
What is alpha blending
Simplest method of blending close to the seam interpolate using an α parameter Iblend = αIleft + (1-α)Iright
48
What is the effect of window size on image blending
if window is too big: ghosting effect, can see the other image underneath if window is too small: aggressively joined together with no smoothing
49
what is the optimal window for image blending
To avoid seams: ■ window = size of largest prominent feature To avoid ghosting: ■ window <= 2*size of smallest prominent feature

COMP37212 - Comp Vis (12 decks)

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