Segmentation of Stochastic Images using ... - Jacobs University

More documents

Recommendations

Info

Bibliography [62] J. Gubner. Probability and random processes for electrical and computer engineers. Cambridge University Press., 2006. [63] H. Harbrecht, M. Peters, and R. Schneider. On the low-rank approximation by the pivoted Cholesky decomposition. Applied Numerical Mathematics, 2011. In press. [64] B. Hayes. A lucid interval. American Scientist, 91(6):484–488, 2003. [65] K. Held, E. Kops, B. Krause, W. Wells, R. Kikinis, and H.-W. Müller-Gärtner. Markov random field segmentation of brain MR images. IEEE Transactions on Medical Imaging, 16(6):878– 886, 1997. [66] G. Herman. Fundamentals of Computerized Tomography: Image Reconstruction from Projections. Advances in pattern recognition. Springer, 2009. [67] M. R. Hestenes and E. Stiefel. Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6):409–436, 1952. [68] T. Hida and N. Ikeda. Analysis on Hilbert space with reproducing kernel arising from multiple Wiener integral. In L. M. Le Cam and J. Neyman, editors, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 1967. [69] H. Holden, B. Øksendal, J. Ubøe, and T. Zhang. Stochastic partial differential equations. A modeling, white noise functional approach. 2nd ed. Universitext. New York, Springer, 2010. [70] O. Holzmann, B. Lang, and H. Schütt. Newton’s constant of gravitation and verified numerical quadrature. Reliable Computing, 2:229–239, 1996. [71] B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17:185– 203, 1981. [72] I. James. The topology of Stiefel manifolds. London Mathematical Society lecture note series. Cambridge University Press, 1976. [73] S. Janson. Gaussian Hilbert Spaces. Cambridge University Press, 1997. [74] I. Jolliffe. Principal Component Analysis. Springer, 2002. [75] O. Juan, R. Keriven, and G. Postelnicu. Stochastic motion and the level set method in computer vision: Stochastic active contours. International Journal of Computer Vision, 69(1):7– 25, 2006. [76] M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, 1(4):321–331, 1988. [77] S. Kay. Fundamentals Of Statistical Signal Processing. Prentice Hall, 2001. [78] R. B. Kearfott. Interval computations: Introduction, uses, and resources. Euromath Bulletin, 2:95–112, 1996. [79] M. Kendall, A. Stuart, J. Ord, and A. O’Hagan. Kendall’s advanced theory of statistics, volume 1. Edward Arnold, 1994. [80] B. Khoromskij and I. Oseledets. Quantics-TT collocation approximation of parameterdependent and stochastic elliptic PDEs. Computational Methods in Applied Mathematics, 10(4):376–394, 2010. 124
Bibliography [81] B. N. Khoromskij and C. Schwab. Tensor-structured Galerkin approximation of parametric and stochastic elliptic PDEs. SIAM Journal on Scientific Computing, 33(1):364–385, 2011. [82] S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi. Gradient flows and geometric active contour models. In Fifth International Conference on Computer Vision, 1995. Proceedings., pages 810–815, 1995. [83] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 220(4598):671–680, 1983. [84] O. M. Knio and O. P. Le Maître. Uncertainty propagation in CFD using polynomial chaos decomposition. Fluid Dynamics Research, 38(9):616–640, 2006. [85] Y. G. Kondratiev, P. Leukert, and L. Streit. Wick calculus in Gaussian analysis. Acta Applicandae Mathematicae, 44:269–294, 1996. [86] K. Krajsek, I. Dedovic, and H. Scharr. An estimation theoretical approach to Ambrosio- Tortorelli image segmentation. In R. Mester and M. Felsberg, editors, Pattern Recognition, volume 6835 of Lecture Notes in Computer Science, pages 41–50. Springer, 2011. [87] T. Kröger, I. Altrogge, O. Konrad, R. M. Kirby, and T. Preusser. Estimation of probability density functions for parameter sensitivity analyses. In H. Hauser, S. Strassburger, and H. Theisel, editors, SimVis, pages 61–74. SCS Publishing House e.V., 2008. [88] D. Landau and K. Binder. A guide to Monte Carlo simulations in statistical physics. Cambridge University Press, 2005. [89] Y. Law, H. Lee, and A. Yip. A multiresolution stochastic level set method for Mumford-Shah image segmentation. IEEE Transactions on Image Processing, 17(12):2289–2300, 2008. [90] C. Li, C. Xu, C. Gui, and M. D. Fox. Level set evolution without re-initialization: A new variational formulation. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20-26 June 2005, pages 430–436, 2005. [91] Z. Liang, P. Lauterbur, I. E. in Medicine, and B. Society. Principles of magnetic resonance imaging: a signal processing perspective. IEEE Press Series in Biomedical Engineering. SPIE Optical Engineering Press, 2000. [92] G. Lin, C.-H. Su, and G. Karniadakis. Predicting shock dynamics in the presence of uncertainties. Journal of Computational Physics, 217(1):260–276, 2006. [93] G. Lin, X. Wan, C.-H. Su, and G. E. Karniadakis. Stochastic computational fluid mechanics. Computing in Science and Engineering, 9(2):21–29, 2007. [94] M. Ljungberg, S. Strand, and M. King. Monte Carlo calculations in nuclear medicine: applications in diagnostic imaging. Medical Science Series. Taylor & Francis, 1998. [95] M. Loève. Probability theory. Springer-Verlag, New York, 4th edition, 1977. [96] R. Malladi, J. A. Sethian, and B. C. Vemuri. Evolutionary fronts for topology-independent shape modeling and recovery. In ECCV ’94: Proceedings of the third European conference on Computer vision (vol. 1), pages 3–13. Springer-Verlag New York, 1994. 125
Page 1:
Segmentation of Stochastic Images u
Page 5:
Abstract The task of segmentation,
Page 8 and 9:
7.5 Segmentation of Stochastic Imag
Page 11 and 12:
Chapter 1 Introduction The developm
Page 13 and 14:
Figure 1.3: This thesis combines fi
Page 15:
the presentation of the stochastic
Page 18 and 19:
Chapter 2 Image Segmentation and Li
Page 20 and 21:
Page 22 and 23:
Page 24 and 25:
Page 26 and 27:
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36 and 37:
Chapter 3 SPDEs and Polynomial Chao
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Chapter 4 Discretization of SPDEs F
Page 50 and 51:
Chapter 4 Discretization of SPDEs 4
Page 52 and 53:
Chapter 4 Discretization of SPDEs a
Page 54 and 55:
Page 56 and 57:
Page 58 and 59:
Chapter 5 Stochastic Images
Page 60 and 61:
Chapter 5 Stochastic Images Figure
Page 62 and 63:
Chapter 5 Stochastic Images like qu
Page 64 and 65:
Chapter 5 Stochastic Images Figure
Page 67 and 68:
Chapter 6 Segmentation of Stochasti
Page 69 and 70:
6.1 Random Walker Segmentation on S
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
6.2 Ambrosio-Tortorelli Segmentatio
Page 79 and 80:
Page 81 and 82:
Page 83 and 84: 6.2 Ambrosio-Tortorelli Segmentatio
Page 85 and 86: 6.2 Ambrosio-Tortorelli Segmentatio
Page 87: 6.2 Ambrosio-Tortorelli Segmentatio
Page 90 and 91: Chapter 7 Stochastic Level Sets whe
Page 92 and 93: Chapter 7 Stochastic Level Sets Fig
Page 94 and 95: Chapter 7 Stochastic Level Sets and
Page 96 and 97: Chapter 7 Stochastic Level Sets rar
Page 100 and 101: Chapter 7 Stochastic Level Sets MC
Page 106 and 107: Chapter 7 Stochastic Level Sets act
Page 108 and 109: Chapter 8 Segmentation of Classical
Page 118 and 119: Chapter 9 Summary, Discussion, and
Page 121 and 122: List of Figures 1.1 Left: CT image
Page 123 and 124: List of Figures 6.2 Mean and varian
Page 125: List of Figures 7.12 Mean (left) of
Page 129 and 130: Appendix A Publications Written Dur
Page 131 and 132: Bibliography [14] L. Ambrosio and M
Page 133: Bibliography [46] B. Echebarria, R.
Page 137 and 138: Bibliography [113] A. Nouy. A gener
Page 139: Bibliography [147] J. Tryoen, O. Le
show all

Segmentation of Stochastic Images using ... - Jacobs University

Create successful ePaper yourself

Delete template?

Save as template?