Fast Approximate Energy Minimization via Graph Cuts

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

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

3. 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. 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 0 + K/2 + K/2 = K (a) 0 4 + 0 + 0 = 4 4 (b)

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

6. 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 matchthat of neighbors –regularization

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

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

9. 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. 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]

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

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

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

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

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

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

19. 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. 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!