active appearance models dhruv batra ece cmu. active appearance models 1.t.f.cootes, g.j. edwards...
DESCRIPTION
Essence of the Idea “Interpretation through synthesis” Form a model of the object/image (Learnt from the training dataset) I. Matthews and S. Baker, "Active Appearance Models Revisited," International Journal of Computer Vision, Vol. 60, No. 2, November, 2004, ppTRANSCRIPT
Active Appearance Models
Dhruv BatraECE CMU
Active Appearance Models
1. T.F.Cootes, G.J. Edwards and C.J.Taylor. "Active Appearance Models", in Proc. European Conference on Computer Vision 1998 (H.Burkhardt & B. Neumann Ed.s). Vol. 2, pp. 484-498, Springer, 1998
2. T.F.Cootes, G.J. Edwards and C.J.Taylor. "Active Appearance Models", IEEE PAMI, Vol.23, No.6, pp.681-685, 2001
3. G.J. Edwards, A. Lanitis, C.J. Taylor, T. F. Cootes. “Statistical Models of Face Images Improving Specificity”, BMVC (1996)
Essence of the Idea “Interpretation through synthesis”
Form a model of the object/image (Learnt from the training dataset)
I. Matthews and S. Baker, "Active Appearance Models Revisited," International Journal of Computer Vision, Vol. 60, No. 2, November, 2004, pp. 135 - 164.
Essence of the Idea (cont.) Explain a new example in terms of the model parameters
So what’s a model
Model
“Shape” “texture”
Active Shape Modelstraining set
Texture Models
warp to mean shape
Random Aside Shape Vector provides alignment
=
43Alexei (Alyosha) Efros, 15-463 (15-862): Computational Photography, http://graphics.cs.cmu.edu/courses/15-463/2005_fall/www/Lectures/faces.ppt
Random Aside Alignment is the key
1. Warp to mean shape
2. Average pixels
Alexei (Alyosha) Efros, 15-463 (15-862): Computational Photography, http://graphics.cs.cmu.edu/courses/15-463/2005_fall/www/Lectures/faces.ppt
Random Aside Enhancing Gender
more same original androgynous more opposite
D. Rowland, D. Perrett. “Manipulating Facial Appearance through Shape and Color”, IEEE Computer Graphics and Applications, Vol. 15, No. 5: September 1995, pp. 70-76
Random Aside (can’t escape structure!)
Alexei (Alyosha) Efros, 15-463 (15-862): Computational Photography, http://graphics.cs.cmu.edu/courses/15-463/2005_fall/www/Lectures/faces.ppt
Antonio Torralba & Aude Oliva (2002)
Averages: Hundreds of images containing a person are
averaged to reveal regularities in the intensity patterns across
all the images.
Random Aside (can’t escape structure!)
Tomasz Malisiewicz, http://www.cs.cmu.edu/~tmalisie/pascal/trainval_mean_large.png
Random Aside (can’t escape structure!)“100 Special Moments” by Jason Salavon
Jason Salavon, http://salavon.com/PlayboyDecades/PlayboyDecades.shtml
Random Aside (can’t escape structure!)“Every Playboy Centerfold, The Decades (normalized)” by Jason Salavon
1960s 1970s 1980sJason Salavon, http://salavon.com/PlayboyDecades/PlayboyDecades.shtml
Back (sadly) to Texture Models
raster scan
Normalizations
PCA Galore
Reduce Dimensions of shape vector
Reduce Dimension of “texture” vector
They are still correlated; repeat..
Object/Image to Parameters
modeling
~80
Playing with the Parameters
First two modes of shape variation First two modes of gray-level variation
First four modes of appearance variation
Active Appearance Model Search Given: Full training model set, new image to be interpreted,
“reasonable” starting approximation
Goal: Find model with least approximation error
High Dimensional Search: Curse of the dimensions strikes again
Active Appearance Model Search Trick: Each optimization is a similar problem, can be learnt
Assumption: Linearity
Perturb model parameters with known amount
Generate perturbed image and sample error
Learn multivariate regression for many such perterbuations
Active Appearance Model Search Algorithm: current estimate of model parameters: normalized image sample at current estimate
Active Appearance Model Search Slightly different modeling:
Error term:
Taylor expansion (with linear assumption)
Min (RMS sense) error:
Systematically perturb and estimate by numerical differentiation
Active Appearance Model Search (Results)