1. Image segmentation using GMM
IBMR 2016 Hwk1

2. Requirement
Input:
Drawing white / red lines on image marking object / background, and the program will separate object from background.
Output
the 2-label (foreground / background) image with black and white.
NO existing GMM code is allowed.

3. Image Segmentation using GMM
Using GMM to model the labeled object/background color distribution.
For unlabeled pixels, GMMs tell that the pixel is much similar to foreground or background.
N是一個Gaussian，pi是weight

4. GMM(Gaussian Mixture Model)

5. EM for GMMs

6. EM algorithm
Randomly choose K (<=5) and initial K Gaussian Models G1…Gk
Use GMM to model the pixel vectors (object or background)
E-step: Evaluate the Responsibilities
M-Step: Re-estimate Parameters. Get G1new ,…,Gknew
Replace G with Gnew
Repeat 2 ~ 5 until converge.
Check Lecture 1 p.23~p.29 for more details.

7. EM algorithm E-step: Evaluate the Responsibilities
M-Step: Re-estimate Parameters
E-step：計算每個點n對每個component k的反應，得到強弱反映tau
M-step：利用E-step算出來的tau，計算出新的mu、sigma和pi

8. Segmentation considering neighbor pixels
Thinks an approach to consider neighbor pixels for segmentation on your own
Simplify of Graph Cut (or you can implement complete Graph Cut)
Output a 2-label image with black and white, and compare accuracy with ground truth
想一個方法將鄰居的點考慮進去，可以參考graph cut的想法作一些簡化或是直接時作graph cut，最後輸出一張黑白（背景/前景）影像，用來跟ground truth比較正確率

9. Grading GMM 80% Segmentation considering neighbor pixels 20% Bonus
Including foreground / background segmentation
Segmentation considering neighbor pixels 20%
Grades depend on the accuracy with ground truth
Bonus
Implement complete Graph cuts algorithm
Due date: 2016/11/03 11:59 p.m.
Upload to E3
Zipped file includes code/report/result image, file named as student number ex zip