6. Multiscale tricks
If it must fail, it must fail early
Iterative Reduction by (re-)Weighting
7.
8. Statistical Model of Shape & Appearance for
constrained search [Tim Cootes]
Shape : point location
Appearance : image patch around point
X = Xavg + StdDev X = Xavg + Pb
P : PCA , b : parameter (reduced search space)
Steps :
Foreach point in shape model:
▪ Find most similar patches along normal direction
▪ Test if current shape fit with learned model
▪ Continue until convergence
9.
10. Hx = z, given x & z, find H
Set of pair of matched points
Overdetermined system of linear equation
RANSAC
Pick small subset
Calculate H
Check whether H is supported by the rest of
population
Select best H with biggest support (minimize
error)
20. Fill patches with highest information content
Structure (edge)
Boundary-patch
High variance : chaotic textures (e.g. grass)
Low variance : plain
21.
22.
23.
24.
25. Math can also means Fun especially when it’s
applied, you don’t know what you’ve
missed
Innovation in Research seeks the balance
between pragmatic and platonic (practical vs
ideal), there are still lot of areas to explore
For everything one can imagine, someone is
making it somewhere
26. DO NOT Afraid!
DO NOT Worry!
DO NOT Hesitate!