Fitting a transformation: feature-based alignment Thursday, September 26 th 2013 Devi Parikh Virginia Tech 1 Slide credit: Kristen Grauman Disclaimer: Many slides have been borrowed from Kristen Grauman, who may have borrowed some of them from others. Any time a slide did not already have a credit on it, I have credited it to Kristen. So there is a chance some of these credits are inaccurate.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Fitting a transformation:feature-based alignment
Thursday, September 26th 2013Devi Parikh
Virginia Tech
1Slide credit: Kristen Grauman
Disclaimer: Many slides have been borrowed from Kristen Grauman, who may have borrowed some of them from others. Any time a slide did not already have a credit on it, I have credited it to Kristen. So there is a chance some of these credits are inaccurate.
Given: initial contour (model) near desired object
a.k.a. active contours, snakes
Figure credit: Yuri Boykov
Goal: evolve the contour to fit exact object boundary
[Snakes: Active contour models, Kass, Witkin, & Terzopoulos, ICCV1987]
Main idea: elastic band is iteratively adjusted so as to• be near image positions with
high gradients, and• satisfy shape “preferences” or
contour priors
Last time: Deformable contours
2
Slide credit: Kristen Grauman
Last time: Deformable contours
Image from http://www.healthline.com/blogs/exercise_fitness/uploaded_images/HandBand2-795868.JPG Kristen Grauman
3
Pros:• Useful to track and fit non-rigid shapes• Contour remains connected• Possible to fill in “subjective” contours• Flexibility in how energy function is defined, weighted.Cons:• Must have decent initialization near true boundary, may
get stuck in local minimum• Parameters of energy function must be set well based on
Alignment problem• We have previously considered how to fit a model to
image evidence– e.g., a line to edge points, or a snake to a deforming contour
• In alignment, we will fit the parameters of some transformation according to a set of matching feature pairs (“correspondences”).
T
xixi
'
Kristen Grauman
41
Image alignment
• Two broad approaches:– Direct (pixel-based) alignment
• Search for alignment where most pixels agree– Feature-based alignment
• Search for alignment where extracted features agree• Can be verified using pixel-based alignment 42
Slide credit: Kristen Grauman
Fitting an affine transformation• Assuming we know the correspondences, how do we
get the transformation?
),( ii yx ),( ii yx
2
1
43
21
tt
yx
mmmm
yx
i
i
i
i
43
Slide credit: Kristen Grauman
An aside: Least Squares ExampleSay we have a set of data points (X1,X1’), (X2,X2’),
(X3,X3’), etc. (e.g. person’s height vs. weight)We want a nice compact formula (a line) to predict X’s
from Xs: Xa + b = X’We want to find a and bHow many (X,X’) pairs do we need?
What if the data is noisy?
'22
'11
XbaXXbaX
'2
'1
2
1
11
XX
ba
XX
Ax=B
.........111
'3
'2
'1
3
2
1
XXX
ba
XXX
overconstrained
2min BAx
Source: Alyosha Efros
44
Fitting an affine transformation• Assuming we know the correspondences, how do we
get the transformation?
),( ii yx ),( ii yx
2
1
43
21
tt
yx
mmmm
yx
i
i
i
i
i
i
ii
ii
yx
ttmmmm
yxyx
2
1
4
3
2
1
10000100
45
Slide credit: Kristen Grauman
Fitting an affine transformation
• How many matches (correspondence pairs) do we need to solve for the transformation parameters?
• Once we have solved for the parameters, how do we compute the coordinates of the corresponding point for ?
• Where do the matches come from?
i
i
ii
ii
yx
ttmmmm
yxyx
2
1
4
3
2
1
10000100
),( newnew yx
Kristen Grauman
46
What are the correspondences?
?
• Compare content in local patches, find best matches.e.g., simplest approach: scan with template, and compute SSD or correlation between list of pixel intensities in the patch
• Later in the course: how to select regions according to the geometric changes, and more robust descriptors.
Kristen Grauman
47
Fitting an affine transformation
Figures from David Lowe, ICCV 1999
Affine model approximates perspective projection of planar objects.