Office: 440 Whittemore Hall
Office hours: Fridays 3:00 pm to 4:00 pm (starting 09/11/2015)
Office: 264 Whittemore Hall
Office Hours: Mondays 10:00 am to 11:30 am
Wednesdays 1:30 pm to 3:00 pm
Announcements Course overview Requirements Schedule Class Projects References
Welcome to ECE 5554!
"To see is to know what is
where by looking"- David Marr
Over the past few decades, machines have come a long way in their ability to "see". Some examples are autonomous navigators like Mars Rover and Google car, medical imaging technologies, pedestrian detectors in vehicles, image search engines, face detectors in cameras and Facebook, aids for the visually impaired, control-free video games, and industrial automation systems.
In this introductory Computer Vision course, we will learn how to "teach machines to see". We will explore several fundamental concepts including image formation, feature detection, segmentation, multiple view geometry, recognition, and video processing. We will use these concepts to build applications that aid machines to see the world around them.
Problem Sets (55% of final grade): You will be given 6 problem sets, one approximately every two weeks. These will involve a combination of conceptual questions and programming problems. The programming problems will provide hands-on experience working with techniques covered in or related to the lectures. All code and written responses must be completed individually and submitted to Scholar. Most problem sets will take significant time to complete. Please start early. Problem Set 0 (PS0) will be worth 5% the final grade, and the remaining 5 problem sets will be worth 10% each.
Project (25% of final grade): Your project can be about applying any of the techniques we studied in class to real world problems. You can also extend a technique, or empirically analyze it. Comparisons between two approaches are also welcome. It is wonderful if you design and evaluate a novel approach to an important existing or new vision problem. Be creative! You must work in teams of two or more. The instructor may make exceptions. If you want to work on a project alone, come talk to the instructor first. The following are deliverables for your project. Only one team-member should submit each deliverable. That is, only one submission (for each deliverable) per team please.
Due Dates: All problem sets/reports are to be submitted by the due date noted on the assignment. Deadlines are firm. Anything from 1 minute to 24 hours is one day late.
Day policy: Throughout the term
you have an allowance of four free late days for your submissions,
meaning you can accrue up to four days in late submissions with no
penalty. For example, you could turn in one assignment four days late,
or two assignments/project proposal/project report webpage each one day late.
Once you have used all your free late days, a late submission will not
be accepted. Please plan
ahead so you can spend your late days wisely. In particular, note that
we expect you will find the earlier assignments easier than those later
in the course. We will count a full additional day as
having passed for submissions 1 minute to 24 hours late. Submissions
that are one day late after all the late days are used up will receive only up to 50% credit. Submissions
that are two days late after all the late days are used up will receive only up to 25% credit. After that, 0 credit.
Exams (15% of final grade): There will be an in-class final exam. The exam is closed everything, but one 8.5 x 11 cheat sheet is allowed (two sides).
Participation and attendance (5% of final grade): Participation in class and regular attendance is expected. If for whatever reason you are absent, it is your responsibility to find out what you missed that day. Note that attendance and participation do factor into the final grade.
Textbook: Computer Vision: Algorithms and Applications, by Rick Szeliski. An electronic copy is available free online here. Some background reading on object recognition is from Kristen Grauman and Bastian Leibe's short book on Visual Object Recognition.
Scholar: The scholar webpage for this course is here (https://scholar.vt.edu/portal/site/vision_fall2015).
|Day/Date||Topic||Readings and links||Lectures||Assignments, Exams, Deadlines|
|Tue 8/25/2015||Course Intro||Sec 1.1-1.3||Intro [ppt]||PS0 out|
|Thu 8/27/2015||Features and filters||Sec 3.1.1-2, 3.2||Linear filters [ppt]||
|Mon 08/31/2015||PS0 due|
||Sec 3.2.3, 4.2
Seam carving paper
Seam carving video
||Sec 3.3.2-4||Edges and binary image analysis [ppt]||PS1 out|
Foundations of Color, B. Wandell
Lotto Lab illusions
|Tue 9/15/2015||Grouping and fitting||Sec 5.2-5.4
|Segmentation and clustering [ppt]||
|Wed 9/16/2015||PS1 due|
|| Sec 4.3.2
Hough Transform demo
Excerpt from Ballard & Brown
|Hough transform [ppt]|
||Sec 5.1.1||Deformable contours [ppt]||PS2 out|
||Sec 2.1.1, 2.1.2, 6.1.1||Alignment and 2D image transformations [ppt]||
|Tue 9/29/2015||Multiple views and motion||Sec 3.6.1, 6.1.4||Homography and image
Notes on homography matrix
|Project proposals due|
||Sec 4.1||Local invariant features 1 [ppt]|
|Mon 10/5/2015||PS2 due|
||Sec 4.1||Local invariant features 2 [ppt]||PS3 out|
||Sec 11.1.1, 11.2-11.5||Image formation [ppt]||
||Sec 11.1.1, 11.2-11.5
Epipolar geometry demo
Audio camera, O'Donovan et al.
Virtual viewpoint video, Zitnick et al.
|Epipolar geometry and stereo [ppt]|
Affine structure fom motion
Epipolar geometry and projective structure from motion
Structure from motion [ppt]
|Mon 10/19/2015||PS3 due|
& Leibe Ch 1-4 (3 is
Grauman & Leibe Ch 5,6
Video Google demo by Sivic et al., paper
|Indexing local features and instance recognition [ppt]|
& Leibe Ch 7,8.1,9.1,11.1
|Intro to category recognition [ppt]||
& Leibe Ch 7,8.1,9.1,11.1
Viola-Jones face detection paper (for additional reference)
|Face detection [ppt]|
& Leibe Ch 11.3 11.4
|Discriminative classifiers for image recognition [ppt]||
& Leibe Ch 11.3 11.4
|Parts-based models [ppt]||PS4 out|
Face recognition: A Literature Survey (pp 1-26)
|Face recognition [ppt]|
|Tue 11/10/2015||Video processing||8.4,12.6.4||Motion and optical flow [ppt]|
|Thu 11/12/2015||No class|
|Mon 11/16/2015||PS4 due|
Davis & Bobick paper: The Representation and Recognition of Action Using Temporal Templates
Stauffer & Grimson paper: Adaptive Background Mixture Models for Real-Time Tracking.
|Background subtraction, action recognition [ppt]|
||5.1.2, 4.1.4||Tracking [ppt]||PS5 out|
|Sat 11/21/2015||Project report due||Project report due
||No class (Thanksgiving)||
||No class (Thanksgiving)|
Convolutional Neural Networks (CNNs) [ppt]: Michael Cogswell
Learning and Using Common Sense [ppt]:
|Wed 12/2/2015||PS5 due|
2:30 pm - 5:30 pm
GoodWin Hall Atrium
|List of class projects
||Visual Question Answering (VQA) + SPOT Evaluations|
||Reading day (no class)|
|Tue 12/15/2015||Final Exam 10:05AM to 12:05PM||Final Exam|
This course closely follows the following course: