Computer Vision: Segmentation
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Mean-Shift, Segmentation as Energy Minimization

Kapitel:

Start Kapitel
00:00:00
Lecture 7: Segmentation as Energy Minimization
00:00:09
Announcements
00:01:16
Course Outline
00:02:09
Recap: Image Segmentation
00:02:57
Recap: K-Means Clustering
00:05:16
Recap: Expectation Maximization (EM)
00:07:51
Recap: EM Algorithm
00:11:18
MoG Color Models for Image Segmentation
00:13:38
Finding Modes in a Histogram
00:16:42
Mean-Shift Segmentation
00:17:12
Mean-Shift Algorithm
00:18:44
Mean-Shift
00:22:18
Real Modality Analysis
00:22:59
Mean-Shift Clustering
00:24:55
Mean-Shift Clustering/Segmentation
00:26:58
Mean-Shift Segmentation Results
00:27:21
More Results (1)
00:27:59
More Results (2)
00:28:05
Problem: Computational Complexity
00:28:47
Speedups (1): Basin of Attraction
00:29:26
Speedups (2)
00:33:00
Summary Mean-Shift
00:39:47
Back to the Image Segmentation Problem...
00:40:54
Topics of This Lecture
00:41:17
Markov Random Fields
00:43:24
MRF Nodes as Pixels
00:45:09
Network Joint Probability
00:47:04
Energy Formulation (1)
00:50:15
Energy Formulation (2)
00:53:54
Energy Minimization
00:55:45
Topics of This Lecture
00:55:51
Graph Cuts for Optimal Boundary Detection
00:59:24
Simple Example of Energy
01:01:46
Adding Regional Properties (1)
01:04:00
Adding Regional Properties (2)
01:04:33
Adding Regional Properties (3)
01:06:02
How to Set the Potentials? Some Examples
01:09:29
Example: MRF for Image Segmentation
01:14:16
Topics of This Lecture
01:14:25
How Does it Work? The s-t-Mincut Problem
01:14:41
The s-t-Mincut Problem
01:15:24
What is the st-mincut?
01:15:37
How to Compute the s-t-Mincut?
01:16:47
History of Maxflow Algorithms
01:19:12
Maxflow Algorithms
01:21:34
Applications: Maxflow in Computer Vision
01:22:22
When Can s-t Graph Cuts Be Applied?
01:25:16
Topics of This Lecture
01:25:21
Dealing with Non-Binary Cases
01:25:49
α-Expansion Move
01:26:22
α-Expansion Algorithm
01:26:43
References and Further Reading