statistical consistency of the normalized eight-point...
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Statistical Consistency of the NormalizedEight-Point Algorithm
W. Chojnacki and M. J. Brooks
School of Computer ScienceThe University of Adelaide
ICIAP 2007
1 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Introduction
Outline
1 Introduction
2 Prerequisites
3 Extended Statistical Model
4 Data Normalization
5 Parameter Estimation
6 Bias
7 Approximate Consistency
8 Conclusion
2 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Introduction
Key Points
Hartley’s normalized eight-point algorithm can be justifiedstatistically
Data normalization =⇒ increase of the consistency ofestimates as the number of data points increases
Uses a statistical model for data distribution
3 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Introduction
Related Work
Longuet-Higgins (Nature, 1981)
Hartley (PAMI, 1997)
Muhlich and Mester (ECCV, 1998)
Chojnacki, Brooks, van den Hengel, Gawley (ICIAP, PAMI,2003)
4 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Prerequisites
Outline
1 Introduction
2 Prerequisites
3 Extended Statistical Model
4 Data Normalization
5 Parameter Estimation
6 Bias
7 Approximate Consistency
8 Conclusion
5 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Prerequisites
Fundamental Matrix
m = [mx, my, 1]T image point
(m, m′) pair of corresponding points
F = [fij] 3× 3 fundamental matrix
Fundamental matrixCaptures
Relative orientation of the camerasInternal geometry of the cameras
Satisfies
Epipolar constraintm′TFm = 0
Rank-2 constraintdet F = 0
6 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Prerequisites
Fundamental Matrix
m = [mx, my, 1]T image point
(m, m′) pair of corresponding points
F = [fij] 3× 3 fundamental matrix
Fundamental matrixCaptures
Relative orientation of the camerasInternal geometry of the cameras
Satisfies
Epipolar constraintm′TFm = 0
Rank-2 constraintdet F = 0
6 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Prerequisites
Fundamental Matrix
m = [mx, my, 1]T image point
(m, m′) pair of corresponding points
F = [fij] 3× 3 fundamental matrix
Fundamental matrixCaptures
Relative orientation of the camerasInternal geometry of the cameras
Satisfies
Epipolar constraintm′TFm = 0
Rank-2 constraintdet F = 0
6 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Prerequisites
Algebraic Least Squares I
{(mn, m′n)}N
n=1 — data points
ALS cost function
JALS(F) = ∑Nn=1(m′T
n Fmn)2
‖F‖2F
‖F‖F = (∑i,j
f 2ij )
1/2 Frobenius norm
ALS estimate
FALS = arg minF 6=0
JALS(F)
Ignores the rank-2 constraint
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Prerequisites
Algebraic Least Squares II
x = [mx, my, m′x, m′
y]T joint image point
based on (m, m′)
u(x) = vec(mm′T) carrier
θ = vec(FT) parameter vector
m′TFm = θTu(x)
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Prerequisites
Algebraic Least Squares III
xn = [mx,n, my,n, m′x,n, m′
y,n]T joint image pointbased on (mn, m′
n)
A =N
∑n=1
u(xn)u(xn)T moment matrix
JALS(θ) =θTAθ
‖θ‖2 ‖θ‖ = (θ21 + · · ·+ θ2
9)1/2
vec(FTALS) = θALS is an eigenvector of A associated with the
smallest eigenvalue
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Prerequisites
Normalized Algebraic Least Squares I
ALS estimates are highly sensitive to noise
Hartley’s modification
Normalize input data prior to running ALS
mn = Tmn m′n = T ′m′
n
T, T ′ 3× 3 data-dependent affine matrices
Apply ALS to the normalized data {(mn, m′n)}N
n=1 and thenback-transform the result
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Prerequisites
Normalized Algebraic Least Squares II
Pre-NALS estimate of F
FALS ALS estimate based on {(mn, m′n)}N
n=1
NALS estimate of F
FNALS = T ′TFALST
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Prerequisites
Normalized Algebraic Least Squares III
Computing Pre-NALS Estimate
xn = [mx,n, my,n, m′x,n, m′
y,n]T joint image pointbased on (mn, m′
n)
A =N
∑n=1
u(xn)u(xn)T moment matrixbased on {xn}N
n=1
θALS = vec(FTALS) is an eigenvector of A associated with the
smallest eigenvalue
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Prerequisites
Reasons for ImprovementExisting Explanations
Hartley — numerical explanation
the smallest eigenvector of A = ∑Nn=1 u(xn)u(xn)T is less
sensitive to small perturbations of the matrix entries than thesmallest eigenvector of A = ∑N
n=1 u(xn)u(xn)T
Chojnacki et al. — statistical explanation
NALS estimate is a minimiser of a cost function, JNALSthe summands of JNALS are more balanced in terms of spreadthan the summands of JALS
uses a statistical model for data distribution
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Extended Statistical Model
Outline
1 Introduction
2 Prerequisites
3 Extended Statistical Model
4 Data Normalization
5 Parameter Estimation
6 Bias
7 Approximate Consistency
8 Conclusion
14 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Extended Statistical Model
Reasons for ImprovementNew Statistical Explanation
Data normalization improves the consistency of estimates
Requires an extended statistical model for data distribution
Inspired by work of Muhlich and Mester on enhancing totalleast squares estimation methods via equilibration
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Extended Statistical Model
Structure of Image Points
mn = nn + ∆mn m′n = n′n + ∆m′
n
nn = c + ∆nn n′n = c′ + ∆n′nn = 1, 2, . . .
nn, n′n “true” locations of ideal data points
∆mn, ∆m′n noise
c, c′ nonrandom “centroids”
∆nn, ∆n′n ideal data-point structural perturbations
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Extended Statistical Model
Structure of Image Points
mn = nn + ∆mn m′n = n′n + ∆m′
n
nn = c + ∆nn n′n = c′ + ∆n′nn = 1, 2, . . .
nn, n′n “true” locations of ideal data points
∆mn, ∆m′n noise
c, c′ nonrandom “centroids”
∆nn, ∆n′n ideal data-point structural perturbations
16 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Extended Statistical Model
Structure of Image Points
mn = nn + ∆mn m′n = n′n + ∆m′
n
nn = c + ∆nn n′n = c′ + ∆n′nn = 1, 2, . . .
nn, n′n “true” locations of ideal data points
∆mn, ∆m′n noise
c, c′ nonrandom “centroids”
∆nn, ∆n′n ideal data-point structural perturbations
16 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Extended Statistical Model
Structure of Image Points
mn = nn + ∆mn m′n = n′n + ∆m′
n
nn = c + ∆nn n′n = c′ + ∆n′nn = 1, 2, . . .
nn, n′n “true” locations of ideal data points
∆mn, ∆m′n noise
c, c′ nonrandom “centroids”
∆nn, ∆n′n ideal data-point structural perturbations
16 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Extended Statistical Model
Statistical Properties
∆mn, ∆m′n, ∆nn, ∆n′n are mutually independent
Zero means
E[∆mn] = E[∆m′n] = E[∆nn] = E[∆n′n] = 0
Anisotropic noise
E[∆mn∆mTn ] = diag(σ2
x , σ2y , 0)
E[∆m′n∆m′T
n ] = diag(σ′2x , σ′2y , 0)
Anisotropic structural perturbations
E[∆nn∆nTn ] = diag(τ2
x , τ2y , 0)
E[∆n′n∆n′Tn ] = diag(τ′2x , τ′2y , 0)
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Data Normalization
Outline
1 Introduction
2 Prerequisites
3 Extended Statistical Model
4 Data Normalization
5 Parameter Estimation
6 Bias
7 Approximate Consistency
8 Conclusion
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Data Normalization
Empirical Means and Deviations
Means
mN =1N
N
∑n=1
mn m′N =
1N
N
∑n=1
m′n
Standard deviations
sx,N =
(1N
N
∑n=1
(mx,n −mx,N)2
)1/2
sy,N =
(1N
N
∑n=1
(my,n −my,N)2
)1/2
s′x,N =
(1N
N
∑n=1
(m′x,n −m′
x,N)2
)1/2
s′y,N =
(1N
N
∑n=1
(m′y,n −m′
y,N)2
)1/2
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Data Normalization
Affine Matrices I
Image-based random affine matrices
TN =
s−1x,N 0 −s−1
x,Nmx,N
0 s−1y,N −s−1
y,Nmy,N
0 0 1
T′
N =
s′−1x,N 0 −s′−1
x,Nm′x,N
0 s′−1y,N −s′−1
y,Nm′y,N
0 0 1
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Data Normalization
Affine Matrices II
Limit nonrandom affine matrices
limN→∞
TN = T
limN→∞
T′N = T ′
almost sure convergence(“except on an event ofprobability zero”)
T =
(σ2x + τ2
x )−1/2 0 −(σ2x + τ2
x )−1/2cx0 (σ2
y + τ2y )−1/2 −(σ2
y + τ2y )−1/2cy
0 0 1
T ′ =
(σ′2x + τ′2x )−1/2 0 −(σ′2x + τ′2x )−1/2c′x0 (σ′2y + τ′2y )−1/2 −(σ′2y + τ′2y )−1/2c′y0 0 1
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Data Normalization
Normalized Image Points
Normalized noisy image points derived from mn and m′n
mn,N = TNmn
m′n,N = T′
Nm′n
n = 1, . . . , N
Normalized “true” image points
nn = Tnn
n′n = T ′n′nn = 1, 2, . . .
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Data Normalization
Normalized Image Points
Normalized noisy image points derived from mn and m′n
mn,N = TNmn
m′n,N = T′
Nm′n
n = 1, . . . , N
Normalized “true” image points
nn = Tnn
n′n = T ′n′nn = 1, 2, . . .
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Data Normalization
Joint Image Points
Joint image points based on (mn, m′n) and (nn, n′n)
xn = [mn,x, mn,y, m′n,x, m′
n,y]T
yn = [nn,x, nn,y, n′n,x, n′n,y]T
Joint image points based on (mn,N, m′n,N) and (nn, n′n)
xn,N = [mn,N,x, mn,N,y, m′n,N,x, m′
n,N,y]T
yn = [nn,x, nn,y, n′n,x, n′n,y]T
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Parameter Estimation
Outline
1 Introduction
2 Prerequisites
3 Extended Statistical Model
4 Data Normalization
5 Parameter Estimation
6 Bias
7 Approximate Consistency
8 Conclusion
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Parameter Estimation
Moment matrices
Pre-NALS moment matrices
AN =1N
N
∑n=1
u(xn,N)u(xn,N)T
BN =1N
N
∑n=1
u(yn)u(yn)T
Normalizing factor N−1
Ensures statistical stabilityDoes not effect the eigenvectors
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Parameter Estimation
Eigenpairs
Eigenvectors and their corresponding eigenvalues
aj,N aj,N the jth eigenpair of AN
bj,N bj,N the jth eigenpair of BNj = 1, . . . , 9
All eigenvectors are
NormalizedArranged in descending order
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Parameter Estimation
Random Estimates
a9,N = vec(FTALS,N) b9,N = vec(FT
N) pre-NALS
FNALS,N = T′TNFALS,NTN FN = T ′TFNT NALS
FNALS,N is a typical estimate based on N noisycorrespondences
FN is a reference value against which to compare FNALS,N
If the {(nn, n′n)}Nn=1 were genuine correspondences bound by a
fundamental matrix FN, then FN would be a natural “true”value with which to gauge FNALS,NNo such FN exists, however.
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Parameter Estimation
Random Estimates
a9,N = vec(FTALS,N) b9,N = vec(FT
N) pre-NALS
FNALS,N = T′TNFALS,NTN FN = T ′TFNT NALS
FNALS,N is a typical estimate based on N noisycorrespondences
FN is a reference value against which to compare FNALS,N
If the {(nn, n′n)}Nn=1 were genuine correspondences bound by a
fundamental matrix FN, then FN would be a natural “true”value with which to gauge FNALS,NNo such FN exists, however.
27 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Parameter Estimation
Random Estimates
a9,N = vec(FTALS,N) b9,N = vec(FT
N) pre-NALS
FNALS,N = T′TNFALS,NTN FN = T ′TFNT NALS
FNALS,N is a typical estimate based on N noisycorrespondences
FN is a reference value against which to compare FNALS,N
If the {(nn, n′n)}Nn=1 were genuine correspondences bound by a
fundamental matrix FN, then FN would be a natural “true”value with which to gauge FNALS,NNo such FN exists, however.
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Parameter Estimation
Random Estimates
a9,N = vec(FTALS,N) b9,N = vec(FT
N) pre-NALS
FNALS,N = T′TNFALS,NTN FN = T ′TFNT NALS
FNALS,N is a typical estimate based on N noisycorrespondences
FN is a reference value against which to compare FNALS,N
If the {(nn, n′n)}Nn=1 were genuine correspondences bound by a
fundamental matrix FN, then FN would be a natural “true”value with which to gauge FNALS,NNo such FN exists, however.
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Bias
Outline
1 Introduction
2 Prerequisites
3 Extended Statistical Model
4 Data Normalization
5 Parameter Estimation
6 Bias
7 Approximate Consistency
8 Conclusion
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Bias
Bias in NALS estimates
AN = {nn, n′n}Nn=1 first N “ideal” data points
E[FNALS,N|AN] the conditional expectationof FNALS,N given AN
DN = ‖E[FNALS,N|AN]− FN‖ bias in the FNALS,N estimates
DN captures the bias conditional on any particulararrangement of the “true” data points {nn, n′n}N
n=1.
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Bias
Bias in NALS estimates
AN = {nn, n′n}Nn=1 first N “ideal” data points
E[FNALS,N|AN] the conditional expectationof FNALS,N given AN
DN = ‖E[FNALS,N|AN]− FN‖ bias in the FNALS,N estimates
DN captures the bias conditional on any particulararrangement of the “true” data points {nn, n′n}N
n=1.
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Bias
Bias in NALS estimates
AN = {nn, n′n}Nn=1 first N “ideal” data points
E[FNALS,N|AN] the conditional expectationof FNALS,N given AN
DN = ‖E[FNALS,N|AN]− FN‖ bias in the FNALS,N estimates
DN captures the bias conditional on any particulararrangement of the “true” data points {nn, n′n}N
n=1.
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Bias
Bias in Pre-NALS Estimates
Bias in the pre-NALS estimate
dN = ‖E[FALS,N|AN]− FN‖
Equivalent definition
dN = ‖E[a9,N|AN]− b9,N‖
Asymptotic behavior of DN is fully controlled by asymptoticbehavior of dN
limN→∞
DN = 0 if and only if limN→∞
dN = 0
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Bias
Approximate Bias in Pre-NALS Estimates
dN is difficult to evaluate
No simple expression for a9,N
Use instead the approximate formula for aj,N
aj,N = bj,N +9
∑k=1k 6=j
bTk,N(AN − BN)bj,N
bj,N − bk,Nbk,N 1 ≤ j ≤ 9
Replace dN by
d∗N =
∥∥∥∥∥ 8
∑k=1
bTk,N(E[AN|AN]− BN)b9,N
b9,N − bk,Nbk,N
∥∥∥∥∥
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Approximate Consistency
Outline
1 Introduction
2 Prerequisites
3 Extended Statistical Model
4 Data Normalization
5 Parameter Estimation
6 Bias
7 Approximate Consistency
8 Conclusion
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Approximate Consistency
Approximate Consistency
Main result
limN→∞
d∗N = 0 a.s.
Two main ingredients of proof
1
g = limN→∞
(b8,N − b9,N)−1 < ∞ a.s.
2
limn→∞
E[AN|AN] = I9 a.s.
Proof details
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Approximate Consistency
Approximate Consistency
Main result
limN→∞
d∗N = 0 a.s.
Two main ingredients of proof
1
g = limN→∞
(b8,N − b9,N)−1 < ∞ a.s.
2
limn→∞
E[AN|AN] = I9 a.s.
Proof details
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Conclusion
Outline
1 Introduction
2 Prerequisites
3 Extended Statistical Model
4 Data Normalization
5 Parameter Estimation
6 Bias
7 Approximate Consistency
8 Conclusion
34 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Conclusion
Summary
Hartley’s famous normalized eight-point algorithm
Has a novel statistical interpretationCan be placed more clearly within the spectrum of methodsavailable
35 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Conclusion
Thank you!
The End
36 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Outline
9 Appendix
37 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Appendix Outline
9 Appendix
38 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Approximate Consistency
d∗N =
∥∥∥∥∥ 8
∑k=1
bTk,N(E[AN|AN]− BN)b9,N
b9,N − bk,Nbk,N
∥∥∥∥∥d∗N ≤
8
∑k=1
|bTk,N(E[AN|AN]− BN)b9,N|
b9,N − bk,N‖bk,N‖
d∗N ≤8
∑k=1
|bTk,N(E[AN|AN]− BN)b9,N|
b9,N − bk,N
39 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Approximate Consistency
d∗N =
∥∥∥∥∥ 8
∑k=1
bTk,N(E[AN|AN]− BN)b9,N
b9,N − bk,Nbk,N
∥∥∥∥∥d∗N ≤
8
∑k=1
|bTk,N(E[AN|AN]− BN)b9,N|
b9,N − bk,N‖bk,N‖
d∗N ≤8
∑k=1
|bTk,N(E[AN|AN]− BN)b9,N|
b9,N − bk,N
39 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Approximate Consistency
d∗N =
∥∥∥∥∥ 8
∑k=1
bTk,N(E[AN|AN]− BN)b9,N
b9,N − bk,Nbk,N
∥∥∥∥∥d∗N ≤
8
∑k=1
|bTk,N(E[AN|AN]− BN)b9,N|
b9,N − bk,N‖bk,N‖
d∗N ≤8
∑k=1
|bTk,N(E[AN|AN]− BN)b9,N|
b9,N − bk,N
39 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Approximate Consistency
lim supN→∞
d∗N ≤ lim supN→∞
max1≤j≤8
1bj,N − b9,N
× lim supN→∞
8
∑k=1
|bTk,N(E[AN|AN]− BN)b9,N|
39 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Approximate Consistency
lim supN→∞
d∗N ≤ g lim supN→∞
8
∑k=1
|bTk,N(E[AN|AN]− BN)b9,N|
39 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Approximate Consistency
lim supN→∞
d∗N ≤ g lim supN→∞
8
∑k=1
|bTk,N(I9 − BN)b9,N|
bTk,N(I9 − BN)b9,N = (1− b9,N)bT
k,Nb9,N = 0
Return
39 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm
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Appendix
Approximate Consistency
lim supN→∞
d∗N ≤ g lim supN→∞
8
∑k=1
|bTk,N(I9 − BN)b9,N|
bTk,N(I9 − BN)b9,N = (1− b9,N)bT
k,Nb9,N = 0
Return
39 W. Chojnacki and M. J. Brooks Consistency of the Normalized Eight-Point Algorithm