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Paper Reading
Dalong Du
Nov.27, 2009
Papers
• Leon Gu and Takeo Kanade. A Generative Shape Regularization Model for Robust Face Alignment. ECCV08.
• Yan Li, Leon Gu, Takeo Kanade. A Robust Shape Model for Multi-view Car Alignment. CVPR09.
A Generative Shape Regularization Model for Robust Face Alignment
Leon Gu and Takeo Kanade
Outline
• Author Introduction.
• Problem Introduction.
• How to do?
• Discussion.
Outline
• Author Introduction.
• Problem Introduction.
• How to do?
• Discussion.
Author Introduction (1/3)
Leon Gu Takeo Kanade(金出武雄 )
Author Introduction (2/3)
• Leon Gu– Ph.D. candidate in the Computer Science
Department at Carnegie Mellon University, advised by Professor Takeo Kanade.
– His main research interest is in developing robust and efficient algorithms for object recognition. A common thread has been the focus on reasoning the shape of visual objects from noisy, real-world images, where the uncertainties over image appearance and imaging conditions are prevalent.
Author Introduction (3/3)
• Takeo Kanade– Director of the Robotics Institute of Carnegie
Mellon University– Wisdom:像外行一样思考,像专家一样实践
Outline
• Author Introduction.
• Problem Introduction.
• How to do?
• Discussion.
Problem Introduction (1/12)
• Face Q consists of N landmark points:– – The geometry information of Q decouples into
two parts:• A canonical shape S
– b– e.g. Or other
linear or nonlinear methods
• A transformation– θ– e.g. similarity s, R, t Or
Affine or others.
x u b b
θ
Problem Introduction (2/12)
• Probabilistic Formulation– Generic alignment problem
•
Where– Pose space Θ is free– Shape space is constrained
• A solution maximizes the posterior
• A chicken and egg problem– A best solution A max posterior
Problem Introduction (3/12)
• In the Eyes of Computer– On the basis of such “noisy observation”,
how can make the best hypothesis (b, θ) ?
Reflectance,Occlusion,Image blur,…..
Noisy feature map
Deformation
Transformation
Image Likelihood
Problem Introduction (4/12)
• A Generative hierarchical model– Deformation
• The magnitude of deformation is controlled by b.
• The canonical shape S is generated from b
through , a process that could be linear or nonlinear.
– Transformation • The transform could be similarity/affine.
– Image Likelihood• Varies with the type of image local feature
– Profile, local image patch, Haar-like feature… …
Deformation
Transformation
Image Likelihood
Problem Introduction (5/12)
• Baseline Model– Linear Deformation
•
Where– Shape prior , Λ is diagonal.– Isotropic shape noise (Probabilistic PCA)– The average residual variance outside of the subspace
, where N is the number of landmark points, M is the subspace dimension.
• {Φ, μ, σ} are learned from training samples.
22
1
1
2
N
mm MN M
Deformation
Transformation
Image Likelihood
Problem Introduction (6/12)
• Baseline Model– Similarity Transform
•
Where– θ={s, R, t} are scale, Rotation, translation coefficients
respectively.– Diagonal observation noise
» measures the noise level of the observation of n-th landmark point.
» » Σ is also learned from training samples.
n
( ( ), )n Dist T S Y
Deformation
Transformation
Image Likelihood
Problem Introduction (7/12)
• Baseline Model– Observed shape Y is generated from
feature point detector.–
• EM– Q-function:– E-step: compute the statistics that are required to
evaluate Q-function.– M-step: maximize Q-function to find the updated shape
and pose.
( , , | ) ( | , ) ( | )p b S Y p Y S p S b Bayes
log ( , , | ) log ( | , ) log ( | )S S S
p b S Y p Y S p S b
Problem Introduction (8/12)
• Alignment algorithm
Problem Introduction (9/12)
• Problems?– Linear deformation model
• Cannot handle faces of rare shapes (babies, etc)• Cannot handle extreme expressions
– Single candidate position for each feature point• Best position may be the one with second strongest
response– This paper extends the generative framework to
handle• Large face shape deformation including extreme
expressions• Multiple candidate positions for each feature point• Identify outliers, like occluded feature points.
Problem Introduction (10/12)
• Handling Extreme Expressions
Problem Introduction (11/12)
• Handling Large Occlusion
Problem Introduction (12/12)
• Handling Real World Images
Outline
• Author Introduction.
• Problem Introduction.
• How to do?
• Discussion.
How to do? (1/12)
• Face Q consists of N landmark points:– – The geometry information of Q decouples into
two parts:• A canonical shape S• A similarity transformation
– Map S from a common reference
frame to the coordinate plane of
the image I
b
θ
How to do? (2/12)
• Make a mixture of constrained Gaussian– Multiple subspace–
How to do? (3/12)
• Allow generate multiple candidate– For n-th landmark
• K candidate positions • denote the whole set of N × K candidates• Set a binary N × K matrix h to specify the “true”
candidate
11 12 1 12
21 22 2 21
1 2 1
... 0 1 ... 0
... 1 0 ... 0( )
... ... ... ... ...... ... ... ...
... 0 0 ... 1
K
K
N N NK NKN KN K N
Q Q Q Q
Q Q Q QQ h Q h
Q Q Q Q
e.g.
How to do? (4/12)
• A new generative hierarchical model
Deformation
Transformation
Image Likelihood
S
1b Lblb ......
nk N KQ Q
z
h
I
Deformation
Transformation
Image Likelihood
( )Q h
How to do? (5/12)
• Deformation– Define prior distribution over the shape S as a
mixture of Gaussian
– Introduce a multinomial distribution z
– Model parameters learned from training samples
, , ,l l l l
0
1
...
0
z
e.g.
How to do? (6/12)
• Similarity Transform
Where– θ={s, R, t} are scale, Rotation, translation
coefficients respectively.– Diagonal observation noise
• measures the noise level of the observation of n-th landmark point.
• • Σ is also learned from training samples and can
updated on fitting phase.– So
n
( ( ), )n Dist T S Y
( )Q h sRS t
How to do? (7/12)
• Image Likelihood– The image likelihood of seeing a landmark
atone particular position Qnk is measured by •
– is generated by feature detector. nk
How to do? (8/12)
• Goal:– Solve b and θ on the basis of the candidate point
set Q.
• MAP problem which can be solved by EM– Posterior with latent variables S, h, z
– Take the expectation of the log over the posterior of the latent variables S, h, z
• Q function:
How to do? (9/12)
• Alignment Algorithm
How to do? (10/12)
• Update Canonical Shape
How to do? (11/12)
• Update Shape Parameters
Shrink by:
How to do? (12/12)
• Identifying Outliers:– Use observation noise model
– Observation noises are unpredictable• Update online
– Change it according to the fitting error between the model prediction and the averaged candidate position
• Define weights to update Canonical Shape– A smaller leads a larger weight to the canonical
shape and less to the observed candidate.
( ( ), )n Dist T S Y
( )Q h sRS t
n
Outline
• Author Introduction.
• Problem Introduction.
• How to do?
• Discussion.
Discussion (1/6)
• Evaluation
Discussion (2/6)
• Handling Extreme Expressions– Number of mixture components is L = 3.
Discussion (3/6)
• Handling Large Occlusion
Discussion (4/6)
• Handling Real World Images
Discussion (5/6)
• Similarity Transform
Where– Diagonal observation noise
• measures the noise level of the observation of n-th landmark point.
• The independence assumption to each landmark is not reasonable.– Markov Network– …
n
( )Q h sRS t
Discussion (6/6)
• The regularization step does not consider the image information anymore
A Robust Shape Model for Multi-view Car Alignment
Yan Li, Leon Gu and Takeo Kanade
Outline
• Problem Introduction.
• How to do?
• Discussion.
Outline
• Problem Introduction.
• How to do?
• Discussion.
Problem Introduction
• Previous shape alignment model– A hypothesis of Gaussian observation noise.– Use all the observed data to fit a regularized
shape.
• This Gaussian assumption is vulnerable to gross feature detection error.
Partial occlusions and spurious background features
Outline
• Problem Introduction.
• How to do?
• Discussion.
How to do? (1/3)
• A hypothesis-and-test approach.– Hypothesis: Bayesian Partial Shape Inference
(BPSI) algorithm•
– Test: The hypotheses are then evaluated to find the one that minimizes the shape prediction error.
1 2{ , ,..., }, { , } { , }N n n nSubsets Q Q Q Q b
How to do? (2/3)
• The observed data– – Random sample from Y
• used to generate hypothesis—shape b and pose θ(s, R, t).
• used to test hypothesis.
• Bayesian Partial Shape Inference (BPSI) algorithm– A MAP problem:– A typical missing data problem can be solved by
EM.
1 2{ , ,..., }, { , }N n n nY Q Q Q Q
pY
hY
How to do? (3/3)
Generate Hypothesis
Test Hypothesis
is the residual between the i-th Corresponding point ofi
and p hY Y
Discussion
Discussion
Thank you!