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Mathematical Methods in Image Processing and Computer Vision

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Numerical Simulation in Physics and Engineering

Part of the book series: SEMA SIMAI Springer Series ((SEMA SIMAI,volume 9))

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Abstract

Image processing and computer vision are growing research fields that take advantage of the increasing power or modern computers linked with sophisticated techniques coming from many fields of expertise and in particular from mathematics. We present an introduction to some problems in computer vision and image processing and to some mathematical techniques and concepts that are nowadays routinely used to approach them.

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Notes

  1. 1.

    All derivatives have to be understood in a weak sense throughout the text.

  2. 2.

    Note that the unknown is now named u and hence I might denote the input image from now on.

  3. 3.

    http://structuralsegm.sourceforge.net/

  4. 4.

    The determination of the true 3D motion of objects is known as scene flow and will not be analyzed in these notes. The interested reader can refer to [26] and references therein.

References

  1. Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20 (1–2), 89–97 (2004)

    MathSciNet  Google Scholar 

  2. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10 (2), 266–277 (2001)

    Article  MATH  Google Scholar 

  3. Chan, T.F., Golub, G.H., Mulet, P.: A nonlinear primal–dual method for total variation–based image restoration. SIAM J. Sci. Comput. 20 (6), 1964–1977 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  4. Chan, T.F., Sandberg, B.Y., Vese, L.A.: Active contours without edges for vector-valued images. J. Vis. Commun. Image Represent. 11 (2), 130–141 (2000)

    Article  Google Scholar 

  5. Cok, D.R.: Signal processing method and apparatus for producing interpolated chrominance values in a sampled color image signal. U.S. Patent No. 4,642,678 (1987)

    Google Scholar 

  6. Collins, R.: A plane-sweep approach to true multi-image matching. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, pp. 358–363 (1996)

    Google Scholar 

  7. Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, New Orleans (1996)

    Book  Google Scholar 

  8. Faugeras, O., Luong, Q.T.: The Geometry of Multiple Images. MIT, Cambridge (2001)

    MATH  Google Scholar 

  9. Ferradans, S., Bertalmio, M., Caselles, V.: Geometry-based demosaicking. IEEE Trans. Imgage Process. 18 (3), 665–670 (2009)

    Article  MathSciNet  Google Scholar 

  10. Gong, M.: Real-time joint disparity and disparity flow estimation on programmable graphics hardware. Comput. Vis. Image Underst. 113 (1), 90–100 (2009)

    Article  Google Scholar 

  11. Gunturk, B.K., Glotzbach, J., Altunbasak, Y., Schafer, R.W., Mersereau, R.M.: Demosaicking: color filter array interpolation. IEEE Signal Proc. Mag. 22 (1), 44–54 (2005)

    Article  Google Scholar 

  12. Gunturk, B.K., Li, X. (eds.): Image Restoration: Fundamentals and Advances. Digital Imaging and Computer Vision. CRC Press, Boca Raton (2012)

    MATH  Google Scholar 

  13. Hamilton, J.F., Adams, J.E.: Adaptive color plane interpolation in single sensor color electronic camera. U.S. Patent No. 5,629,734 (1997)

    Google Scholar 

  14. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  15. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)

    Article  Google Scholar 

  16. Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26 (2), 147–159 (2004)

    Article  MATH  Google Scholar 

  17. Laroche, C.A., Prescott, M.A.: Apparatus and method for adaptively interpolating a full color image utilizing chrominance gradients. U.S. Patent No. 5,373,322 (1994)

    Google Scholar 

  18. Menon, D., Calvagno, G.: Color image demosaicking: an overview. Signal Process. Image Commun. 26 (8–9), 518–533 (2011)

    Article  Google Scholar 

  19. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42, 577–685 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  20. Noma, A., Graciano, A.B., Cesar, R.M. Jr., Consularo, L.A., Bloch, I.: Interactive image segmentation by matching attributed relational graphs. Pattern Recognit. 45 (3), 1159–1179 (2012)

    Article  Google Scholar 

  21. Papadakis, N., Baeza, A., Gargallo, P., Caselles, V.: Polyconvexification of the multi-label optical flow problem. In: Proceedings of the International Conference on Image Processing (ICIP), Hong Kong, pp. 765–768 (2010)

    Google Scholar 

  22. Pock, T., Schoenemann, T., Graber, G., Bischof, H., Cremers, D.: A convex formulation of continuous multi-label problems. In: Proceedings of the European Conference on Computer Vision (ECCV), Marseille (2008)

    Google Scholar 

  23. Rockafellar, R.T.: Monotone operators and the proximal point algorithm. SIAM J. Control Optim. 14 (5), 877–898 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  24. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  25. Tikhonov, A.N., Arsenin, V.Y.: Solutions of ill-posed problems. Winston & Sons, Washington (1977)

    MATH  Google Scholar 

  26. Vedula, S., Baker, S., Rander, P., Collins, R., Kanade, T.: Three-dimensional scene flow. IEEE Trans. Pattern Anal. Mach. Intell. 27 (3), 475–480 (2005)

    Article  Google Scholar 

  27. Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L1 optical flow. Pattern Recognit. (Proc. DAGM 2007) LNCS 4713, 214–223 (2007)

    Google Scholar 

  28. Zhu, M., Chan, T.: An efficient primal-dual hybrid gradient algorithm for total variation image restoration. Tech. Rep. 08–34, UCLA (2008)

    Google Scholar 

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Acknowledgements

The author is grateful to the organizers and scientific committee of the Jacques-Louis Lions Spanish-French school for the invitation. This research was Partially supported by Spanish MINECO grant MTM 2014-54388. This work is dedicated to the memory of Vicent Caselles.

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Baeza, A. (2016). Mathematical Methods in Image Processing and Computer Vision. In: Higueras, I., Roldán, T., Torrens, J. (eds) Numerical Simulation in Physics and Engineering. SEMA SIMAI Springer Series, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-32146-2_3

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