1. PCA: Hand modelling
Nikzad B.Rizvandi

2. Hand tracking Looking for a tracking system: Face tracking
Lips tracking
Pose tracking

3. Dataset

4. Pre-processing Represent images with landmarks Images have:
Different sizes
Different rotations
Different locations

5. Pre-processing: manual labeling
Placing landmarks
Manually placing N1 landmarks
In the same location
Placing intermediate points
Automatic edge detection
of edges between landmarks
Points in total: N
X=(x0, y0, x1, y1,…, xN-1, yN-1)T

6. Pre-processing Represent images with landmarks
Alignment since images have:
Different sizes
Different rotations
Different locations

7. Pre-processing: Alignment (1/3)
Remove X-Y translation
Move the Center of mass to coordinate origin
Remove Scaling:
Calculate L2-norm of the shape

8. Pre-processing: Alignment (2/3)
Remove Rotation:
Needs reference shape :
Calculate Singular Value Decomposition(SVD)
Rotation Matrix =

9. Pre-processing: Alignment (3/3)
Align each shape to first shape by rotation, scaling, and translation
Repeat
Calculate the mean shape
Normalize the orientation, scale, and origin of the current mean to suitable defaults
Realign every shape with the current mean
Until the mean shape does not change in two iterations

10. PCA to reduce dimension (1/4)
The 2N elements are highly correlated, so we can represent them much more compactly
Principal Component Analysis (PCA)
Reduces the number of parameters from 2N to M, where M<<2N

11. PCA to reduce dimension (2/4)

12. PCA to reduce dimension (3/4)
The shape of the model is given by:
is the mean shape,
most important eigenvectors,
contains the shape parameters

13. PCA to reduce dimension (4/4)
40 shapes in database
56 landmarks (112 parameters)
95% of variation by 6 new parameters
112 parameters  6 new parameters

14. More information
Dataset:
Paper: Nikzad B.Rizvandi, A.Pizurica, W.Philips, “Deformable Shape Description Using Active Shape Model (ASM)”, 18th ProRISC Workshop on Circuits, Systems and Signal Processing (ProRISC), 2007, Netherlands