Class schedule
Date | Topic | Readings and Annoucements | ||
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1/30 | Introduction and logistics |
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Module 1: Image Formation | ||||
2/4 | Light and color: I - Spectral basis of light - Color perception in the human eye |
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2/6 | Class cancelled, snow day | |||
2/11 | Light and color: II - Tristimulus theory and color spaces - Color phenomenon |
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2/13 | Image formation: I - Pinhole camera model - Qualitative properties - Cameras with lens - Lens phenomenon |
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2/18 | Image formation: II - Lens flaws and modern lenses - Analog and digital color sensing - Alignment and demosacing |
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2/20 | No Class, Monday schedule | |||
Module 2: Image Processing | ||||
2/25 | Python tutorial | |||
2/27 | Image processing: I - Spatial and bightness quantization - Improving brightness and contrast - The convolution operation - Denoising |
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3/4 | Image processing: II - Sharpening - Hybrid Images - Edge detection |
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3/6 | Local features: I - Role of local features - Harris corner detector |
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3/11 | Local features: II - Harris corner detector - Invariance and equivariance |
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3/13 | Midterm in class |
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3/18 | Spring break |
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3/20 | Spring break |
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3/25 | Local features: III - Invariance and equivariance - Blob detector - Eliminating rotational ambiguity - The SIFT descriptor |
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3/27 | Matching and alignment: I - The SIFT descriptor - Matching and ratio test - Model fitting using RANSAC |
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4/1 | Matching and alignment: II - Image transformations - Estimating transformations from matching - Image warping - Modern approaches |
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4/3 | Optical flow - Motion field and optical flow - Lucas-Kanade optical flow - Alternate flow techniques - Depth estimation, point tracking, video interpolation |
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Module 3: Image Understanding | ||||
4/8 | Recognition by alignment - Instance matching - Vocabulary trees - Beyond instance matching |
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4/10 | Visual recognition - What is recognition? - Brief history of recognition - Current trends |
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4/15 | Classical machine learning: I - The machine learning framework - Nearest neighbor classifiers - Hyperparameters search |
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4/17 | Classical machine learning: II - Linear classifiers - Loss function, regularization, optimization |
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4/22 | Image representations - Role of representations - Classical representations (HOG and BoVW) |
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4/24 | Deep learning: I - Multi-layer perceptrons - Activations, Loss functions, optimization - Practical issues - Convolutional networks |
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4/29 | Deep Learning: II - Convolutional networks - LeNet - AlexNet and ImageNet challenge |
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5/1 | Deep Learning: III - AlexNet and ImageNet challenge - Visualizing deep networks - Transfer learning - Modern CNNs |
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5/6 | Object detection - Sliding-window detectors - Region-based detectors - Datasets and benchmarks |
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5/8 | Guest lecture by Aaron Sun |
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5/14 | Final, 1- 3pm, LGRC A301 |