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Interactive Image Segmentation using Graph Cuts

Interactive Image Segmentation using Graph Cuts. PRASA 2009. Mayuresh Kulkarni and Fred Nicolls Digital Image Processing Group University of Cape Town. Outline. Image Segmentation Problem Our Approach Graph cuts and Gaussian Mixture Models Results and Discussion Future Research.

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Interactive Image Segmentation using Graph Cuts

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  1. Interactive Image Segmentation using Graph Cuts PRASA 2009 MayureshKulkarni and Fred Nicolls Digital Image Processing Group University of Cape Town

  2. Outline • Image Segmentation Problem • Our Approach • Graph cuts and Gaussian Mixture Models • Results and Discussion • Future Research

  3. What is foreground?

  4. Image Segmentation

  5. Our Approach Image properties eg. colour, texture Difference between adjacent pixels 8 – pixel neighbourhood Region information Boundary information Pixel connectivity Graph Cuts Segmentation Cost Function : E(A) = λ R(A) + B(A)

  6. Graph Cuts Source (foreground) Pixel connectivity (boundaries) Inter-pixel weights (boundaries) SourceandSinkweights (regions) Cost Function : E(A) = λ R(A) + B(A) Sink (background)

  7. Gaussian Mixture Models Background GMM Foreground GMM

  8. Gaussian Mixture Models Foreground GMM pf pb Log Likelihood Ratio = log(K *pf/pb) Background GMM

  9. GMM components • Greyscale images • Intensity values • Intensity values and MR8 filters • Colour images • RGB values • G, (G-R), (G-B) values • Luv values • MR8 filters

  10. Boundary information • Inter-pixel weights • Edge detection • Difference between adjacent pixels • Gradient • Pixel connectivity

  11. Results Κ= 0.01 Κ= 0.1 Κ= 1

  12. Results Original Image Luv and MR8 (Fscore = 0.921) Luv (Fscore = 0.934) RGB, Luv and MR8 (Fscore = 0.916)

  13. Results Original Image RGB, Luv and MR8 (Fscore = 0.906) RGB (Fscore = 0.951) Luv (Fscore = 0.945)

  14. Analysis of Results • Accurate segmentation achieved • Components in the GMM depend on image • Segmentation can be controlled using K and λ

  15. Future Research • Different grid (non-pixel grid) • Ratio cuts • Exploring other statistical models • ObjCut – segmenting particular objects

  16. References • Y. Boykov and M. P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In ICCV, volume 1, pages 105–112, July 2001. • Yuri Boykov and Vladimir Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell., 26(9):1124–1137, 2004. • PushmeetKohli, Jonathan Rihan, Matthieu Bray, and Philip H. S. Torr. Simultaneous segmentation and pose estimation of humans using dynamic graph cuts. International Journal of Computer Vision, 79(3):285–298, 2008. • H. Permuter, J. Francos, and I. Jermyn. Gaussian mixture models of texture and colour for image database. In ICASSP, pages 25–88, 2003. • D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proc. 8th Int’l Conf. Computer Vision, volume 2, pages 416–423, July 2001. • Carsten Rother, Vladimir Kolmogorov, and Andrew Blake. “GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph., 23(3):309–314, August 2004.

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