Textbook:
Edward R. Dougherty, Random Processes for Image and Signal
Processing, IEEE Press 1999. ISBM: 0-8194-2513-3. It is not necessary
to buy this textbook but I recommend you to have one very good math
book on continuous stochastic processes.
Lecture Notes & Homeworks
Lecture 00. Image Analysis framework
Lecture
01. Gaussian random fields
Lecture
02. Linear operators on fields
Lecture
03. Kernel smoothing I.
Lecture
04. Numerical implementation of kernel smoothing
Lecture
05. Diffusion equations
Lecture
06. Iterated kernel smoothing
Lecture
07. Isotropic kernels in manifolds
Lecture
08. Kernel smoothing in manifolds
Lecture
09. Simulating Gaussian fields
Lecture
10. Hilbert Space.
Lecture
11. Karhunen Loeve expansion I.
Lecture
12. Karhunen Loeve expansion II.
Lecture
13. Kernel Smoothing II.
Lecture
14. Kernel Smoothing III.
Lecture
15. Diffusion smoothing I.
Lecture
16. Diffusion smoothing II.
Lecture
17. Laplace Operator.
Lecture
18. Multiple Comparisions I.
Lecture
19. Multiple Comparisions II.
Lecture
20. Bivariate smoothing on sphere I.
Lecture
21. Bivariate smoothing on sphere II.
Lecture
22. Brownian motion II.
Lecture
23. Maxima of random fields I.
Lecture
24. Maxima of random fields II.
Lecture
25. Curve Modeling I.
Lecture
26. Curve Modeling II.
Lecture
27. Diffusion smoothing on manifolds II.
Lecture
28. Diffusion smoothing on manifolds III.
Lecture
29. Finite element method
Lecture
30. Curve modeling III.
Lecture
31. Curve modeling IV.
Lecture
32. EM algorithm I.
Lecture
33. EM algorithm II.
Lecture
33. EM algorithm III appendum.
Lecture
34. Anisotropic smoothing
Lecture
35. Smoothing periodic functional data.
Lecture
36. Smoothness of spatial noise.
Lecture
37. Anisotropic smoothing II.
Lecture
38. Image registration I.
Lecture
39. EM algorithm IV..
Lecture
40. Image registration II.
Lecture
41. References