ECE 5554 / ECE 4554: Computer Vision Fall 2016
Class Schedule
| Date | Topic | Lectures | Reading and Links | Assignments | |
| Aug 23 (Tuesday) | Introduction to computer vision | [PPT] [PDF] | |||
| Interpreting Intensity | |||||
 | Aug 25 (Thursday) | Light, shading, and color | [PPT] [PDF] | S2.2 (light) S2.3.2 (color) | |
 | Aug 30 (Tuesday) | Image filters in spatial domain | [PPT] [PDF] | S3.2 (linear filtering) S3.3 (non-linear filtering) | |
 | Sept 1 (Thursday) | Image filters in frequency domain | [PPT] [PDF] | S3.4 (fourier transforms) S2.3.3 (compression) | |
 | Sept 6 (Tuesday) | Templates and image pyramids | [PPT] [PDF] | S3.5.2 (image pyramids) S8.1.1 (pyramid alignment) | |
 | Sept 8 (Thursday) | Edge detection | [PPT] [PDF] | S4.2 | |
| Correspondence and Alignment | |||||
 | Sept 13 (Tuesday) | Interest points | [PPT] [PDF] | S4.1 | |
 | Sept 15 (Thursday) | Feature tracking, optical flow | [PPT] [PDF] | S4.1.4 S8.1, S8.4 | |
 | Sept 20 (Tuesday) | Fitting and alignment | [PPT] [PDF] | S6.1 and S2.1 | HW 1 due Mon |
 | Sept 22 (Thursday) | Alignment and object instance recognition | [PPT] [PDF] | S14.3.2 | |
| Perspective and 3D Geometry | |||||
 | Sept 27 (Tuesday) | Camera models | [PPT] [PDF] | S2.1.5 | |
 | Sept 29 (Thursday) | Single-view geometry and calibration | [PPT] [PDF] | ||
 | Oct 4 (Tuesday) | Image stitching | [PPT] [PDF] | S9 | HW 2 due Mon |
 | Oct 6 (Thursday) | Epipolar geometry, stereo | [PPT] [PDF] | S11 | |
 | Oct 11 (Tuesday) | Multi-view Stereo and Structure from motion | [PPT] [PDF] | S7 | |
| Grouping and Segmentation | |||||
 | Oct 13 (Thursday) | Gestalt Cues. Clustering, and Image Segmentation | [PPT] [PDF] | ||
 | Oct 18 (Tuesday) | EM Algorithm, Mixture of Gaussians | [PPT] [PDF] | HW 3 due Mon | |
 | Oct 20 (Thursday) | MRFs and Graph Cut | [PPT] [PDF] | ||
| Recognition and learning | |||||
 | Oct 25 (Tuesday) | Face recognition | [PPT] [PDF] | ||
 | Oct 27 (Thursday) | Image features and categorization | [PPT] [PDF] | Final Project Proposal due | |
 | Nov 1 (Tuesday) | Machine learning crash course | [PPT] [PDF] | HW 4 due Wed | |
 | Nov 3 (Thursday) | Object detection with statistical templates | [PPT] [PDF] | S5.3 | |
 | Nov 8 (Tuesday) | Part-based models and pose estimation | [PPT] [PDF] | S14.1 S14.2 | |
 | Nov 10 (Thursday) | Visual tracking | [PPT] [PDF] | S14.1 | |
| Advanced Topics | |||||
 | Nov 15 (Tuesday) | Deep learning (Convolutional neural networks) | [PPT] [PDF] | HW 5 due Mon | |
| Nov 17 (Thursday) | Action recognition | [PPT] [PDF] | |||
| Nov 22,24 | No class (Thanksgiving break) | ||||
| Nov 29 (Tuesday) | 3D scenes and context | [PPT] [PDF] | |||
| Dec 1 (Thursday) | Final project presentation | ||||
| Dec 6 (Tuesday) | Class Summary, Important Open Problems, and Feedback | [PPT] [PDF] | |||
| Dec 8 (Thursday) | No class (Reading day) | ||||
| Dec 13 (Tuesday) | Final Exam | ||||
| Dec 15 (Thursday) | Final project report due |
Calendar view
Acknowledgements
The lecture slides build upon many preceding efforts by other instructors, including Derek Hoiem (UIUC), James Hays (Georgia Tech), Steve Seitz (UW), Kristen Grauman (UT Austin), and Devi Parikh (Virginia Tech). Feel free to use and modify any of the slides for academic and research purposes. Please do credit the original sources where appropriate