Image Analysis Class (lectures 2013/2015) by Fred Hamprecht at at Universität Heidelberg

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source: Universität Heidelberg      2013年4月19日
The Image Analysis Class 2013 by Prof. Fred Hamprecht. It took place at the HCI / Heidelberg University during the summer term of 2013.

1.1 Introduction  - Examples 11:03
1.2 Human early vision 48:45
1.3 Image representations 1:09:20
2.1 Patches in Image Analysis 38:31
2.2 Texture Synthesis 43:58
2.3 Non-Local Means for Image Denoising 37:38
2.4 BM3D for Image Denoising 9:56
3.1 Unitary transformations 12:05
3.2 The Fourier Transform 53:27
3.3 The Discrete Fourier Transform (DFT) 1:05:10
3.4 2D-DFT: Application to Images 40:17
4.1 Fourier Transform 51:22
4.2 Time Frequency Decompositions 35:17
4.3 The Wavelet Transform 1:36:43
5.1 Watershed 53:07
5.2 Maximally Stable Extremal Regions 18:47
5.3 Mathematical Morphology 13:33
5.4 Minkowski Functionals 14:52
6.1 Markov Random Fields (MRFs) 57:16
6.2 Gaussian Markov Random Fields (GMRF) 25:13
6.3 Intrinsic GMRFs (IGMRF) 1:22:31
7.1 Factor Graphs 26:18
7.2 Fields of Experts 1:17:24
7.3 Discrete-Valued MRFs 28:14
7.4 MAP inference via Integer Linear Programming (ILP) 52:35
8.1 Integer Linear Programs (continued) 51:25
8.2 Pseudo Boolean Functions (PBFs) 21:20
8.3 Quadratic PBFs with submodular terms 5:15
8.4 Max-Flow / Min-Cut 29:32
8.5 Graph Cuts 1:05:56
9.1 Introduction 7:32
9.2 Example model: Tracking by assignment 36:13
9.3 Structured Support Vector Machine (structSVM) 56:25
9.4 Structured Learning: Applications 23:15
10.1 Light Fields 1:05:24
10.2 Coded Aperture Imaging 21:50
10.3 Compressive Sensing 21:53
9.1 Markov Random Fields 39:22
9.2 Markov Random Fields (cont.) 37:31
10.1 Branch-and-Bound Method for Integer Linear Programming 45:31
10.2 Interior Point Methods for LPs  44:06
12.1 Markov Random Fields with Non-Binary Random Variables 52:04
12.2 Markov Random Fields with Non-Submodular Pairwise Factors 38:13
13 Solving Tree-Shaped MRFs 43:28
14.1 LP Relaxation in Primal and Dual Space 44:56
14.2 LP Relaxation in Dual Space (cont.)  42:21
15.1 Gaussian Markov Random Fields 43:55
15.2 Gaussian Markov Random Fields (cont.) 44:01
16 Gaussian Markov Random Fields (cont.) 1:08:16