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Paint Selection Jiangyu Liu, Jian Sun, Heung- Yeung Shum

ACM ToG 2009. Paint Selection Jiangyu Liu, Jian Sun, Heung- Yeung Shum. Presenter : Jae- Hyuck Park Robotics Program, KAIST surpluseng@outlook.com. Intro Selection Optimization Result Q&A. Interactive Image Segmentation. Receive info from user when segmentation

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Paint Selection Jiangyu Liu, Jian Sun, Heung- Yeung Shum

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  1. ACM ToG 2009 Paint SelectionJiangyu Liu, Jian Sun, Heung-Yeung Shum Presenter : Jae-Hyuck Park Robotics Program, KAIST surpluseng@outlook.com

  2. IntroSelection Optimization Result Q&A Interactive Image Segmentation • Receive info from user when segmentation • ex. Lazy Snapping, Grabcut • Paint Selection is also interactive image segmentation algorithm and very powerful. (Why??)

  3. IntroSelection Optimization Result Q&A Intuitive User Interface • Just dragging on what you want to select

  4. IntroSelection Optimization Result Q&A Intuitive User Interface • When delete region, dragging using right button. cf. Blue and red lines in the figure are not seen in real program

  5. IntroSelection Optimization Result Q&A Real Time Interaction • Lazy Snapping, GrabCut Interaction T < 0.2s Interaction T < 1.0s > 20Mp 1.5GHz 3.0GHz < 1Mp 2004 2009

  6. IntroSelection Optimization Result Q&A Real Time Interaction • Paint Selection • Maintain response time with increase of pixels X-axis number of pixels Y-axis response time(s)

  7. IntroSelection Optimization Result Q&A Real Time Interaction • Performance Comparisons for a 20Mp image X-axis number of user interaction Y-axis response time(s)

  8. IntroSelection Optimization Result Q&A Real Time Interaction • Progressive Selection • Update selection region when mouse dragging

  9. IntroSelection Optimization Result Q&A Real Time Interaction • Not progressive selection ( Laze Snapping ) • Scribble – Wait – Scribble – Wait ……………….. WAIT WAIT WAIT

  10. IntroSelection Optimization Result Q&A Real Time Interaction • Key of fast response time • Progressive selection algorithm • Multilevel banded graph-cut • Local optimization • Multi core graph-cut • Adaptive band unsampling

  11. IntroSelection Optimization Result Q&A Progressive Selection Algorithm • Object – computing a new selection F’ in the background U

  12. IntroSelection Optimization Result Q&A Progressive Selection Algorithm • The binary labels X = {xp} of the image are obtained by minimizing an energy E(x) [Boykov and Jolly 2001] Data term Contrast term

  13. IntroSelectionOptimization Result Q&A Optimization • Multilevel Banded Graph Cuts [Lombaert et al. 2005] • Fast and low consumption of memory than [Boykov 2001]’s graph cuts

  14. IntroSelectionOptimization Result Q&A Optimization • Multilevel Banded Graph Cuts with Progressive Selection Global [Lombaert et al. 2005] Local

  15. IntroSelectionOptimization Result Q&A Optimization • Multi-core graph-cut • Parallel version of [Boykov 2001]’s graph cuts • Parallelizing the dynamic-tree algorithm for grid graphs • Partitioning the graph • Find augmenting paths in parallel • Reusing the trees between iterations by “adoption”

  16. IntroSelectionOptimization Result Q&A Optimization • Adaptive Band Upsampling • Using Joint Bilateral Upsampling(JBU) [ Kopf et al. 2007 ] L + 1 L Result Adaptive band JBU result

  17. IntroSelection OptimizationResult Q&A Result • Performance Comparison 40MP Image 30MP Image Lazy Snapping Paint Selection (Progressive selection only) (b) + dual core graph-cut (c) + adaptive band upsampling

  18. IntroSelection OptimizationResult Q&A Result • Total time comparison with Lazy Snapping(100%)

  19. IntroSelection OptimizationResult Q&A Result • Comparison with Photoshop Quick Selection(middle)

  20. IntroSelection OptimizationResult Q&A Result • Limitation

  21. IntroSelection Optimization ResultQ&A Q&A

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