Office: 440 Whittemore Hall
Office hours: Fridays from 2:30 PM to 3:30 PM
Office Hours: Mondays and Wednesdays from 11:00 AM to 12:00 @ WHIT 215
Announcements Course overview Requirements Schedule References
*** Best Project Prizes ***
Computer Vision Applications for UAV Control,
Mapping and Road Recognition (Matt Frauenthal and Scott Radford)
Optical Character Recognition for Math Equations (Allan Kirchhoff)
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 are welcome to work with a partner. If you need help with ideas for your project please come talk to the instructor and/or TA. The following are deliverables for your project:
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
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 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. No submissions will be accepted after all the late days are used up.
Exams (15% of final grade): There will be an in-class final exam.
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.
|Day/Date||Topic||Readings and links||Lectures||Assignments, Exams, Deadlines|
|Tue 8/27/2013||Course Intro||Sec 1.1-1.3||Intro [ppt]||PS0 out|
|Thu 8/29/2013||Features and filters||Sec 3.1.1-2, 3.2||Linear filters [ppt]|
|Mon 9/2/2013||PS0 due|
|Tue 9/3/2013||Sec 3.2.3, 4.2
Seam carving paper
Seam carving video
|Thu 9/5/2013||Sec 3.3.2-4||Edges and binary image analysis [ppt]||PS1 out. File check script|
|Tue 9/10/2013||Sec 10.5
|Thu 9/12/2013||Sec 2.3.2
Foundations of Color, B. Wandell
Lotto Lab illusions
|Tue 9/17/2013||Grouping and fitting||Sec 5.2-5.4
|Segmentation and clustering [ppt]|
|Wed 9/18/2013||PS1 due|
|Thu 9/19/2013|| Sec 4.3.2
Hough Transform demo
Excerpt from Ballard & Brown
|Hough transform [ppt]|
|Tue 9/24/2013||Sec 5.1.1||Deformable contours [ppt]||PS2 out. File check script|
|Thu 9/26/2013||Sec 2.1.1, 2.1.2, 6.1.1||Alignment and 2D image transformations [ppt]|
|Tue 10/1/2013||Multiple views and motion||Sec 3.6.1, 6.1.4||Homography and image warping [ppt]. Notes on homography matrix||Project proposals due|
|Thu 10/3/2013||Sec 4.1||Local invariant features 1 [ppt]|
|Mon 10/7/2013||PS2 due|
|Tue 10/8/2013||Sec 4.1||Local invariant features 2 [ppt]||PS3 out|
|Thu 10/10/2013||Sec 11.1.1, 11.2-11.5||Image formation [ppt]|
|Tue 10/15/2013||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 [ppt]|
Affine structure fom motion
Epipolar geometry and projective structure from motion
Stereo and Structure from motion [ppt]
|Mon 10/21/2013||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]|
|Thu 10/24/2013|| Grauman
& 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 11/12/2013||Video processing||8.4,12.6.4||Motion and optical flow [ppt]|
Davis & Bobick paper: The Representation and Recognition of Action Using Temporal Templates
Stauffer & Grimson paper: Adaptive Background Mixture Models for Real-Time Tracking.
|Bacground substraction, action recognition [ppt]|
|Mon 11/18/2013||PS4 due|
|Tue 11/19/2013||5.1.2, 4.1.4||Tracking [ppt]||PS5 out|
|Thu 11/21/2013||Summary and discussion [ppt]|
|Sat 11/23/2013||Project report due||Project report due|
|Tue 11/26/2013||No class (Thanksgiving)|
|Thu 11/28/2013||No class (Thanksgiving)|
|Tue 12/3/2013||Guest Lectures|
|Wed 12/04/2013||PS5 due|
|Thu 12/5/2013||Guest Lectures|
|Tue 12/10/2013||Poster Presentations @ WHIT 236 from 3PM to 6PM||Poster Presentations @ WHIT 236 from 3PM to 6PM|
|Thu 12/12/2013||Reading day (no class)|
|Fri 12/13/2013||Final Exam 10:05AM to 12:05PM||Final Exam|
This course closely follows the following course: