1 formation et analyse d’images session 2 daniela hall 26 september 2005
TRANSCRIPT
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Formation et Analyse d’ImagesSession 2
Daniela Hall
26 September 2005
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Course Overview
• Session 1: – Overview– Human vision – Homogenous coordinates– Camera models
• Session 2:– Tensor notation– Image transformations– Homography computation
• Session 3:– Reflection models– Color spaces
• Session 4:– Pixel based image analysis
• Session 5:– Gaussian filter operators– Scale Space
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Course overview
• Session 6:– Contrast description– Hough transform
• Session 7:– Kalman filter
• Session 8:– Tracking of regions, pixels, and lines
• Session 9:– Stereo vision
• Session 10:– Epipolar geometry
• Session 11: exercises and questions
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Trifocal Tensor
• A tensor is used in 3d position estimation with multiple cameras. The (trifocal) tensor encapsulates all the (projective) geometric relations between 3 camera views independent of the scene.
• Reference– book: R. Hartley, A.Zisserman: Multiple view geometry in
computer vision, Appendix 1, Cambridge University Press, 2000
– Exists on-line http://www.robots.ox.ac.uk/~vgg/hzbook/hzbook1.html
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Tensor notation
• In tensor notation a superscript stands for a column vector
• a subscript for a row vector (useful to specify lines)
• A matrix is written as
3
2
1
3,2,1)(
p
p
p
pP iii
),,( 321 lllLi
),,( 321
3
2
1
33
32
31
23
22
21
13
12
11
jjj
i
i
iji
MMM
M
M
M
mmm
mmm
mmm
M
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Tensor notation
• Tensor summation convention:– an index repeated as sub and superscript in a
product represents summation over the range of the index.
• Example: 3
32
21
1 plplplPL ii
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Tensor notation
• Scalar product can be written as
• where the subscript has the same index as the superscript. This implicitly computes the sum.
• This is commutative
• Multiplication of a matrix and a vector
• This means a change of P from the coordinate system i to the coordinate system j (transformation).
33
22
11 plplplPL i
i
iii
i LPPL
iji
j PMP
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Line equation
• In R2 a line is defined by the equation
• In homogenous coordinates we can write this as
• In tensor notation we can write this as
0 cbyax
0
1
),,(
y
x
cbaPLT
0iiPL
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The tensor operator Eijk and Eijk
• The tensor Eijk is defined for i,j,k=1,...,3 as
123 ofn permutatio oddan isrst if 1,-
123 ofn permutatioeven an isrst if 1,
distinct are t ands,r, unless 0
rstE
3 2 1 1 3 22 1 3
1 2 3 2 3 1 3 1 2
evenodd
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Determinant in tensor notation
jjjji mmmM 321 ,,
kjiijk
ji mmmEM 321)det(
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Cross product in tensor notation
bac
kjijkii baEbac )(
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Example
• Line equation in tensor notation
kjijki QPEL
1
,
1
2
1
2
1
q
q
Qp
p
P kj
0
),,(
321
12213
1131132
1
321
222332
lylxl
qpqpl
qpqpqpl
qpqpqpl
lllLi
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Example
• Intersection of two lines
• L: l1x+l2y+l3=0, M: m1x+m2y+m3=0
• Intersection:
• Tensor:
• Result:
1221
3113,
1221
2332
mlml
mlmly
mlml
mlmlx
kjijki MLEP
12213
31132
23321
mlmlp
mlmlp
mlmlp
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Translation
• Classic
• Tensor notation
• T is a transformation from the system A to B
• Homogenous coordinates
y
x
tyy
txx
12
12
1
1
1
100
10
01
1
2
2
y
x
ty
tx
y
x
1,2,3 BA, with ABA
B PTP
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Rotation
• Homogenous coordinates
1)cos(1)sin(
1)sin(1)cos(
2
2
yaxay
yaxax
1
1
1
100
0)cos()sin(
0)sin()cos(
1
2
2
y
x
aa
aa
y
x
• Classic
• Tensor notation1,2,3 BA, with AB
AB PRP
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Image transformation
For each position Pd in the destination image we searchthe pixel color I(Pd).
Source image Destination image
Tsd
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Image transformation
First we compute a position Ps in the source image.
dsd
s PTP
Source image Destination image
Tsd
1100
)cos()sin(
)sin()cos(
1d
d
yyx
xyx
s
s
y
x
tasas
tasas
y
x
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Image transformation
• P is not integer.• How do we compute I(Pd)=I(Ps)?• Answer: by a linear combination of the
neighboring pixels I(Psi) (interpolation).
Ps0 Ps1
Ps3Ps2
Ps
Pd
Tsd
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Interpolation methods
• 0th order: take value of closest neighbor– fast, applied for binary images
• 1st order: linear interpolation and bi-linear interpolation
• 3rd order: cubic spline interpolation
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1D linear interpolation
Ps0 Ps1
Ps3Ps2
Ps
position P
intensity I(P)
Ps0 Ps Ps1
)()()())(1(
)()()()(
)()(
1000
00
01
ssssss
ssssd
ss
PIPPPIPP
PIPPmPIPI
PIPIm
Gradient
Pixel color
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2D linear interpolation
Ps0 Ps1
Ps3Ps2
Ps
x
intensity I(P)
Ps0
Ps
Ps1
)()()()()(
)()(
)()(
000
02
01
sssyy
ssxx
sd
ssy
ssx
PIPPmPPmPIPI
PIPIm
PIPIm
Gradient
Pixel color
I(Ps)
y
Psx
Psy
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Bi-linear interpolationPs0 Ps1
Ps3Ps2
Ps
)()()()()(
,)1(
),1(),1)(1(
3210
0
0
sssss
yssy
xssx
PDIPCPBPAIPI
dxdyDdydxC
dydxBdydxA
PPdy
PPdx
The bilinear approach computes the weighted average of the four neighboring pixels. The pixels are weighted according tothe area.
AB
CD
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Higher order interpolation
• Cubic spline interpolation takes into account more than only the closest pixels.
• Result: more expensive to compute, but image has less artefacts, image is smoother.
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Homographie: projection from one plane to another
• Homographie HBA is bijective QB = HB
A PA
jj
jjB
jj
jjB
A
j
j
jAB
AB
B
PHPHy
PHPHx
P
H
H
H
PH
w
wy
wx
32
31
3
2
1
/
/
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Homography computation
• H can be computed from 4 point correspondences.
Ps1 Ps2
Ps3Ps4
Rd1 Rd2
Rd3Rd4Source image (observed)
Destination image (rectified)
4,3,2,1 and 2,1 with ,0 iSRHPRHP DiSD
SiDiSD
Si
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Homography computation
• H is 3x3 matrix and has 8 degrees of freedom (homogenous coordinates)
• gives 8 equations and one solution for H.
4,3,2,1 and 2,1 with ,0 iSRHPRHP DiSD
SiDiSD
Si
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Application: Rectifying images
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Applications