00:00:00
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Lecture 8: Recognition with Global Representations
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00:00:07
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Reminder
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00:04:00
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Course Outline
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00:04:53
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Recap: MRFs for Image Segmentation
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00:08:40
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Recap: Energy Formulation
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00:11:17
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Recap: How to Set the Unary Potentials?
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00:12:43
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Recap: How to Set the Pairwise Potentials?
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00:17:17
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Recap: Graph-Cuts Energy Minimization
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00:23:05
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Recap: When Can s-t Graph Cuts Be Applied?
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00:24:44
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GraphCut Applications: "GrabCut"
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00:27:06
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GrabCut: Data Model
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00:28:45
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GrabCut: Coherence Model
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00:30:36
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Iterated Graph Cuts
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00:32:00
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GrabCut: Example Results
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00:32:58
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Applications: Interactive 3D Segmentation
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00:35:29
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Topics of This Lecture: Object Recognition
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00:36:47
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Object Recognition
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00:37:44
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Challenges
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00:40:05
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Appearance-Based Recognition
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00:41:12
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Global Representation
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00:44:29
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Color: Use for Recognition
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00:45:25
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Color Histograms
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00:48:03
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Color Normalization
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00:49:19
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Color Normalization: Observation
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00:50:14
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Color Histograms: Robust representation
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00:51:14
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Color Histograms: Use for recognition
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00:52:25
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Topics of This Lecture: Recognition using histograms
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00:52:58
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Recognition Using Histograms: Histogram comparison
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00:53:35
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Recognition Using Histograms: With multiple training views
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00:54:13
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What is a Good Comparison Measure?
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00:57:32
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Comparison Measures: Euclidean Distance
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01:01:10
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Comparison Measures: Mahalanobis Distance
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01:05:22
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Comparison Measures: Chi-Square
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01:07:54
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Comparison Measures: Bhattacharyya Distance
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01:10:36
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Comparison Measures: Kullback-Leibler
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01:12:34
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Comparison Measures: Histogram Intersection
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01:14:15
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Comparison Measures: Earth Movers Distance (1)
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01:17:32
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EMD Computation
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01:19:40
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Comparison Measures: Earth Movers Distance (2)
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01:20:45
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Summary: Comparison Measures
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01:21:43
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Comparison for Image Retrieval
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01:23:56
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Histogram Comparison
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01:25:32
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Summary: Recognition Using Histograms
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01:26:55
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Topics of This Lecture
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01:27:00
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Localization by Histogram Backprojection
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01:28:03
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Color-Based Skin Detection
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01:29:19
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Discussion: Color Histograms
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01:30:57
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Topics of This Lecture
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