Image Matting algorithms from Bayesian to Deep Learning

1. NotKind Introduction
for Image Matting
Segmentation은 아니라구

2. Image Matting is
From

3. Image Matting is
Image foreground background
Only deciding alpha! [0,1]

4. Image Matting is
Image foreground background
Only deciding alpha! [0,1]

5. Matting is commonly with
Trimap!
For decide
Foreground/Uncertain/Background

6. History of Matting
A long time ago, …

7. Approaches – Sampling Based
> Mishima Method 1993 – patent
Calculating closest F/B Distance for decide alpha : globally
* Feature
- First method of matting
- Look feature globally
- Developed for Blue screen matting

8. Approaches – Sampling Based
> Knockout software - not sure 1998…?
Calculating closest F/B Distance for decide alpha : locally sampled
* Feature
- First method of matting with Trimap

9. Jumping matting history
Closed Form Matting 2006
- Locally Matting
KNN Matting 2012
- nonLocal Matting

10. Approaches – Deep Learning Age
> DCNN Matting - 2016
Mix Local-Nonlocal information
* Feature
- First method of matting with Deep Learning
Input is
RGB + KNN Matting + Closed Form Matting

11. Approaches – Deep Learning Age
> Deep Image Matting - 2017
* Feature
Fully Deep Learning
Refinement Stage is for alternative Image Guide Filter
Input is
RGB + Trimap

12. Want more?
http://people.inf.ethz.ch/aksoyy/
Meet ‘Yağız Aksoy’ who starts from 2012
or
https://www.ecse.rpi.edu/~rjradke/cvfxcourse.
html kind lecture for matting