1. Lecture 05 29/11/2011 Shai Avidan Roy Josef Jevnisek הבהרה : החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.

2. Motivation Left imageRight image

3. Motivation ResultGround truth

4. Motivation

5. GRAPH CUTS Key researchers: Ramin Zabih, Yuri Boykov, Vladimir Kolmogorov and Olga Veksler

6. From Image to Graph

7. Graph Cuts Min-Cut = Max-flow Maximal flow is easier to find… Ford Fulkerson ( similar to what I did… ) Push Relabel

8. Graph Cuts in Computer Vision S T P3P2 P1P4 Let’s Generalize: S T More Generally:

9. Graph Cuts in Computer Vision Vision is all about Multi-Label Problems… !! GC for binary problem yields global minimum !!

10. Graph Cuts in Computer Vision ,  -swap 1.Start with an arbitrary labeling 2.Cycle through every label pair ( ,  ) in some order 1.Find the lowest E labeling within a single ,  -swap 2.Go there if this has lower E than the current labeling 3.If E did not decrease in the cycle - done. otherwise, go to step 2

11. Graph Cuts in Computer Vision S T P3P2 P1P4

12. Graph Cuts in Computer Vision  -expansion 1.Start with an arbitrary labeling 2.For each label  in some order 1.Find the lowest E labeling within a single  -expansion 2.Go there if this has lower E than the current labeling 3.If E did not decrease in the cycle - done. otherwise, go to step 2

13. Graph Cuts in Computer Vision S T P3P2 P1P4

14. Graph Cuts in Computer Vision What is the big deal ?! ,  -swap  -expansion