Out-of-plane Rotations

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# Out-of-plane Rotations - PowerPoint PPT Presentation

2. 1. 3. 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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## PowerPoint Slideshow about ' Out-of-plane Rotations' - tangia

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Presentation Transcript

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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

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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Projective Geometry

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Homographies
• Homographies hold both for object or camera movements
• The points must be coplanar

H

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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)

Model Coordinates

Image Coordinates

ASM Image Model (Similarity)

Gradient normal to the shape contour

Projective transformations

Do not preserve angles nor distance relationships

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Image Matching

H

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Results

ASM

• Training and test on multi-view data
• Cross validation

PASM

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Comparison to related work

Ratios with respect to error on frontal images

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Results training just a single view (frontal)
• Training set: Frontal
• Test set: Multilple views

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Conclusions on PASM
• 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