Gestalt principles, k-Means clustering, feature spaces, Mixture of gaussians / EM algorithm
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00:00:00
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Lecture 6: Segmentation |
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00:01:24
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Course Outline |
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00:02:17
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Recap: Chamfer Matching |
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00:04:46
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Recap: Hough Transform |
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00:07:42
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Recap: Hough Transform Polar Parametrization |
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00:08:34
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Hough Transform for Circles |
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00:11:39
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Hough Transform for Circles: unknown radius |
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00:13:15
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Hough Transform for Circles: known direction |
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00:14:09
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Hough Transform for Circles: algorithm |
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00:15:50
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Example: Detecting Circles with Hough (1) |
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00:17:02
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Example: Detecting Circles with Hough (2) |
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00:19:50
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Voting: Practical Tips |
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00:22:45
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Hough Transform: Pros and Cons |
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00:25:23
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Generalized Hough Transform |
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00:26:55
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Generalized Hough Transform: Algorithm |
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00:27:59
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Example: Generalized Hough Transform |
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00:30:18
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Application in Recognition |
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00:34:34
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Topics of This Lecture |
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00:35:34
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Examples of Grouping in Vision |
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00:37:41
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Similarity |
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00:38:29
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Proximity |
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00:38:47
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Symmetry |
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00:39:51
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Common Fate |
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00:40:24
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The Gestalt School |
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00:42:59
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Gestalt Theory |
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00:44:45
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Gestalt Factors |
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00:47:32
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Continuity through Occlusion Cues (1) |
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00:48:17
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Continuity through Occlusion Cues (2) |
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00:48:52
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The Ultimate Gestalt? |
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00:49:41
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Image Segmentation |
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00:50:27
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The Goals of Segmentation |
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00:51:26
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Topics of This Lecture |
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00:51:35
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Image Segmentation: Toy Examples |
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00:54:21
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(find cluster centers) |
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00:55:55
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Clustering |
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00:57:05
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K-Means Clustering |
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00:58:43
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Segmentation as Clustering |
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01:00:36
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K-Means Clustering: Java demo |
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01:00:49
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K-Means++ |
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01:03:07
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Feature Space: intensity |
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01:03:40
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Feature Space: color |
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01:04:16
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Feature Space: texture |
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01:05:44
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Smoothing Out Cluster Assignments |
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01:08:58
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Feature Space: intensity+position |
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01:09:29
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K-Means Clustering Results |
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01:10:44
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Summary K-Means |
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01:15:46
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Topics of This Lecture |
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01:16:06
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Probabilistic Clustering |
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01:17:25
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Mixture of Gaussians |
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01:21:08
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Expectation Maximization (EM) |
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01:23:15
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EM Algorithm |
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01:25:26
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Applications of EM |
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01:26:00
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Segmentation with EM |
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01:27:00
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Summary: Mixtures of Gaussians, EM |
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01:29:27
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Topics of This Lecture |
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01:29:36
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References and Further Reading |