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00:00:00
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Lecture 12: Local Features |
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00:00:07
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Course Outline |
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00:01:44
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Recap: Sliding-Window Object Detection |
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00:02:08
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Classifier Construction: Many Choices... |
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00:02:29
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Recap: AdaBoost |
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00:04:20
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Recap: AdaBoost Feature+Classifier Selection |
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00:07:38
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Recap: Viola-Jones Face Detector |
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00:10:34
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Limitations of Sliding Windows (continued) |
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00:11:45
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Limitations (continued) |
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00:15:15
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Topics of This Lecture |
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00:15:42
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Motivation |
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00:17:28
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Application: Image Matching |
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00:18:24
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Harder Case |
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00:18:46
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Harder Still? |
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00:20:27
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Application: Image Stitching |
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00:22:25
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General Approach |
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00:24:16
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Common Requirements |
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00:25:37
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Invariance: Geometric Transformations |
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00:27:11
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Levels of Geometric Invariance |
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00:30:23
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Requirements |
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00:32:33
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Many Existing Detectors Available |
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00:33:44
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Keypoint Localization |
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00:34:53
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Finding Corners |
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00:35:50
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Corners as Distinctive Interest Points |
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00:38:03
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Harris Detector Formulation |
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00:46:55
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Harris Detector Formulation |
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00:50:20
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What Does This Matrix Reveal? |
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00:52:34
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General Case |
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00:54:35
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Interpreting the Eigenvalues |
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00:56:40
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Corner Response Function |
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00:58:47
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Window Function w(x,y) |
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01:00:57
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Summary: Harris Detector [Harris88] |
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01:07:06
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Harris Detector: Workflow |
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01:09:34
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Harris Detector - Responses [Harris88] |
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01:11:32
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Harris Detector: Properties |
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01:14:59
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Hessian Detector [Beaudet78] |
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01:16:55
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Hessian Detector - Responses [Beaudet78] |
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01:18:42
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Topics of This Lecture |
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01:19:47
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References and Further Reading |