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00:00:00
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Lecture 4: Gradients & Edges |
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00:00:58
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Announcements |
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00:09:38
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Course Outline |
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00:10:28
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Topics of This Lecture |
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00:11:24
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Recap: Gaussian Smoothing |
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00:12:58
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Recap: Smoothing with a Gaussian |
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00:14:26
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Recap: Effect of Filtering |
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00:17:51
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Recap: Low-Pass vs. High-Pass |
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00:18:53
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Topics of This Lecture |
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00:19:16
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Motivation: Fast Search Across Scales |
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00:19:53
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Recap: Sampling and Aliasing |
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00:23:00
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Recap: Resampling with Prior Smoothing |
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00:26:32
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The Gaussian Pyramid |
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00:28:00
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Gaussian Pyramid - Stored Information |
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00:29:56
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Summary: Gaussian Pyramid |
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00:35:07
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The Laplacian Pyramid |
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00:40:47
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Laplacian ~ Difference of Gaussian |
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00:42:57
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Topics of This Lecture |
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00:43:18
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Note: Filters are Templates |
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00:44:32
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Where's Waldo? |
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00:48:03
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Correlation as Template Matching |
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00:52:46
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Topics of This Lecture |
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00:53:50
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Derivatives and Edges... |
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00:55:54
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Differentiation and Convolution |
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01:00:32
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Partial Derivatives of an Image |
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01:03:54
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Assorted Finite Difference Filters |
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01:06:54
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Image Gradient |
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01:13:26
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Effect of Noise |
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01:14:46
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Solution: Smooth First |
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01:16:09
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Derivative Theorem of Convolution |
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01:17:13
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Derivative of Gaussian Filter |
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01:18:01
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Derivative of Gaussian Filters |
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01:18:36
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Laplacian of Gaussian (LoG) |
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01:19:51
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Summary: 2D Edge Detection Filters |
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01:20:52
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Topics of This Lecture |
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01:21:22
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References and Further Reading |