Date |
Lecture (click for notes) |
Readings |
Assignments |
Thu, Sep 15 |
Introduction to computer vision |
Ch. 1.1 |
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Tue, Sep 20 |
Image formation and capture |
Ch. 2.2.3 – 2.3 |
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Thu, Sep 22 |
Convolution and filtering |
Ch. 3.2, 3.4, 4.2 |
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Tue, Sep 27 |
Feature detectors and descriptors |
Ch. 4, Trucco & Verri,
Ch. 4.1 – 4.3,
SIFT paper (optional) |
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Thu, Sep 29 |
Fitting, Hough transforms, RANSAC |
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Tue, Oct 4 |
Image alignment and stitching |
Ch. 6.1.1 – 6.1.4; Ch. 9 (optional);
Multires blending paper
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Thu, Oct 6 |
Intro to recognition |
Ch. 14 |
Assignment 1 due |
Tue, Oct 11 |
Classification |
Dalal & Triggs paper |
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Thu, Oct 13 |
Part-based models |
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Tue, Oct 18 |
Texture |
Ch. 10.5;
Efros & Leung paper;
Efros & Freeman paper;
Image Quilting
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Thu, Oct 20 |
Segmentation and clustering |
Ch. 5.2 – 5.4;
Martin et al. segmentation paper;
Shi and Malik normalized cuts paper
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Tue, Oct 25 |
Motion, Optical Flow, Tracking I |
Ch. 8.4; Lucas-Kanade paper;
Ch. 8.1 – 8.3 (optional)
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Thu, Oct 27 |
Tracking II |
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Assignment 2 due |
Tue, Nov 1 |
No class - fall break |
Thu, Nov 3 |
No class - fall break |
Tue, Nov 8 |
Intro to 3D vision; stereo |
Ch. 11.1 – 11.4; Ch. 12 (optional) |
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Thu, Nov 10 |
Multiview reconstruction |
Ch. 11.6; Voxel coloring paper |
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Tue, Nov 15 |
Camera geometry and calibration |
Ch. 2.1; Ch. 6.3 (optional) |
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Thu, Nov 17 |
3D features and shape matching |
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Tue, Nov 22 |
Active 3D scanning methods |
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Thu, Nov 24 |
No class - Thanksgiving |
Tue, Nov 29 |
Introduction to Deep Learning |
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Thu, Dec 1 |
Deep Learning II: Layer types and training |
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Tue, Dec 6 |
Deep Learning III: Initialization, Architectures, Applications |
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Thu, Dec 8 |
Deep Learning IV: Object Detection and Segmentation |
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Tue, Dec 13 |
Deep Learning V: Segmentation |
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Assignment 3 due |
Thu, Dec 15 |
Deep Learning VI |
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Sun, Dec 18 |
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Assignment 4 due |
Thu, Jan 12 |
Final project presentations |
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Fri, Jan 13 |
Final project presentations |
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Tue, Jan 17 |
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Project reports due |