Date post: | 26-Dec-2015 |
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Out-of-plane Rotations Environment
constraints● Surveillance
systems● Car driver images
ASM:● Similarity does not
remove 3D pose ● Multiple-view
database Other approaches
● Non-linear models● 3D models: multiple
views
AV@CAR Database
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1
Projective Geometry
Geometric operations by means of linear algebra 2D points are 3-
component vectors
Multiple views of the same planar object can be related by homographies
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1 2
1 2
, , , , ... ,
...
...
1 1 ... 1
TL Li i i i i i i
Li i i
Li i i i
x y x y x y
x x x
y y y
u
U
1
2 3
4
i ij jU H U
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2
Homographies
Homographies hold both for object or camera movements
The points must be coplanar
H
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3
Coplanar face model
Silhouette points are excluded (out of main plane)
Half the nose points are excluded (easy occlusion)
First iteration: At least 8 correspondences to compute H (4 2D-points)
H
1H
Model Coordinates Image Coordinates
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4
Image Matching
ASM Image Model (Similarity) Gradient normal to the shape
contour Projective transformations
Do not preserve angles nor distance relationships
H
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5
AV@CAR Database (40 people)
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Results
PASM
ASM
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Training and test on multi-view data
Cross validation
7
Comparison to related work
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Ratios with respect to error on frontal images
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Results training just a single view (frontal)
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Training set: Frontal Test set: Multilple views
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Analysis of the single-view case
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Conclusions on PASM
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If multi-view dataset available Almost invariant to rotations up to 60 degrees
Training only on frontal views Considerably reduces (50%) variation of ASM due to
viewpoint
Left-right rotations better handled than up-down nodding
Very difficult to compare to other results
Points used for alignment can affect performance Not considerable for expected ASM precision 11
How reliable is the result?
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12