Fundamentals of computer vision, including image formation, elements of human vision, low-level image processing, and pattern recognition techniques. Advanced topics include modern visual features, graphical models, model-based and data-driven approaches, and contextual inference, as well as illustrative examples of successes and challenges in applications. CSC 449, a graduate-level course, requires additional readings and assignments (including a course project).
Please check pre-requisites on the official course listing page. Some knowledge of linear algebra and basic statistics is helpful but we will review these subjects in the class (please discuss with me if you are not sure). For assignments and projects, the most common programming languages are Matlab, C or C++. Note that Matlab provides an image processing toolbox and is available on most CS department undergraduate and research networks.
| week | dates | chapter | key topics | CSC 249 | CSC 449 | notes |
| 1 | 1/17 | 1, 2 | overview; image formation | |||
| 2 | 1/22, 1/24 | 3, 4 | color vision; low-level image processing | |||
| 3 | 1/29, 1/31 | 4, notes (Forsyth) | texture; Intro to (review of) probability | |||
| 4 | 2/5, 2/7 | Duda & Hart | pattern recognition concepts (incl. classifiers) | |||
| 5 | 2/12, 2/14 | 5, 6 | modern visual features | |||
| 6 | 2/19, 2/21 | 9, 10 | mid-level vision (incl. segmentation, Hough Transform) | |||
| 7 |
2/26, 2/28 |
notes & special lecture | recognizing faces and people | |||
| 8 | 3/5, 3/7 | review/midterm exam | ||||
| 9 | 3/12, 3/14 | Spring Break | ||||
| 10 | 3/19, 3/21 | midterm solutions, 12 | image registration | selecting course survey topics | selecting course project topics | 1-page proposal |
| 11 | 3/26, 3/28 | guest lecture by Prof. Jinwei Gu | computational photography, computer graphics | |||
| 12 | 4/2, 4/4 | 11, 7, 8 | tracking, stereo & 3D | |||
| 13 | 4/9, 4/11 | 18, notes | recognizing objects, scenes, actions and events | |||
| 14 | 4/16, 4/18 | notes | data-driven computer vision | |||
| 15 | 4/23, 4/25 | 21, notes & special lecture | content-based image annotation & retrieval (or alternatively medical image analysis) | or attribute learning in vision | ||
| 16 | 4/30, 5/2 | notes & special lecture | contextual inference in computer vision | |||
| 17 | 5/7, 5/9 | final presentations & reports | impress us! | 5/7 | 5/9 | |
| 18 | 5/14, 5/16* | final presentations & reports | impress us! | 5/14, 5/16* |
Instructor: Prof. Jiebo Luo, CSB Rm 611, x65784
TA: Lam Tran
Lectures: Tuesdays and Thursdays, 12:30-13:45, CSB 601
Office hours: after classes (15:00-16:00) or by appointment (use email).
Grading (total 100%)
Required textbook:
Computer Vision: A Modern Approach, 2/E
ISBN-10: 013608592X
Publisher: Prentice Hall, 2012
Recommended textbook:
Pattern Classification, 2/E
Richard O. Duda, Peter E. Hart and David G. Stork
ISBN-10: 0471056693
Publisher: John Wiley & Sons, 2001
Recommended papers (use IEEE Xplore on the university network for free access to these and other papers):
Longuet-Higgins Prize recipients
(CVPR annual award for 'fundamental contributions in Computer Vision that Have Withstood the Test of Time')
Computer Vision Online Resources (papers, data, etc.)