Image segmentation using GMM IBMR 2016 Hwk1

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.

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

GMM(Gaussian Mixture Model)

EM for GMMs

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.

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

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

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 0466628.zip