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