1. Fast Approximate Energy Minimization via Graph Cuts
M.S. Student, Hee-Jong Hong
May 29, 2013

2. Contents Introduction Previous Works Proposed Method Experiment
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Contents
Introduction
Previous Works
Proposed Method
Experiment
Conclusion

3. Introduction Local Method Global Method Sum of Squared Differences
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Introduction
Local Method
Sum of Squared Differences
Sum of Absolute Differences
Zero-mean Normalized Cross-Correlation
Global Method
Dynamic Programming (One Dimensional Problem)
Graph Cuts (Every Epipolar Line)

4. Introduction Global Optimization V(a,b) = V(b,c) = K/2 V(a,c) = K
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Introduction
Global Optimization
V(a,b) = V(b,c) = K/2
V(a,c) = K
(d)
Sum Of Local Energy
Sum Of Global Energy
(a)
0 + K/2 + K/2 = K
(b) 4
= 4

5. Introduction Dynamic Programming 1 2 3 4 Disparity A Image Row
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Introduction
Dynamic Programming 1 2 3 4
Disparity
A Image Row

6. Introduction Energy Minimization
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Introduction
Energy Minimization
Another global approach to improve quality of correspondences
Assumption: disparities vary (mostly) smoothly
Minimize energy function: Edata+lEsmoothness
Edata: how well does disparity match data
Esmoothness: how well does disparity match that of neighbors – regularization

7. Introduction Energy Definition in Stereo
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Introduction
Energy Definition in Stereo

8. Previous Works S T Max Flow / Min Cut A graph with two terminals
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Previous Works
Max Flow / Min Cut
“source”
A graph with two terminals S T
“sink”

9. Previous Works Labeling
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Previous Works
Labeling
For each pixel, either the F or G edge has to be cut
Only one edge label per pixel can be cut (otherwise could be added

10. Swap Move & Expansion Move
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Proposed Method
Swap Move & Expansion Move

11. Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
𝜶−𝜷 𝑺𝒘𝒂𝒑

12. Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
𝜶−𝜷 𝑺𝒘𝒂𝒑

13. Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
𝜶 − 𝑬𝒙𝒑𝒂𝒏𝒔𝒊𝒐𝒏

14. Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
𝜶 − 𝑬𝒙𝒑𝒂𝒏𝒔𝒊𝒐𝒏

15. Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Move

16. Experiment Energy Definition Data Term :
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Experiment
Energy Definition
Data Term :
Smoothness Term : Static Cues (Weighted Potts)

17. Experiment Static Cues Potts 0?1? unkown Static Cues
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Experiment
Static Cues
Potts
1Pixel Move
0?1?
unkown
Static Cues
Give Higher Smoothness Factor to Continues Intensity

18. Experiment Expansion Move Swap Move
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Experiment
Expansion Move
Swap Move

19. Experiment Expansion Move & Swap Move Normalized Corr & Annealling
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Experiment
Expansion Move
&
Swap Move
Normalized Corr
&
Annealling

20. Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Experiment

21. Conclusion Performs well on a variety of computer vision problems
Visual Object Tracking using Adaptive Correlation Filters [CVPR 2010]
Conclusion
Performs well on a variety of computer vision problems
Image Restoration, Stereo, and Motion
Very Faster than Annealing

22. Thank you!