|Instructor: Kristen Grauman
Office location: GDC 4.726
Office hours: Tues 3-4 and by appointment
|TA: Dongguang (Nick) You
Office location: GDC 3.818D
Office hours: Tues 3-4 pm, Wed 4-5 pm
TA: Paul Choi
Office hours location: GDC 3rd floor lab (by printers)
Office hours: Mon 2:30-3:30 pm, Thurs 3:30-4:30 pm
Please use Piazza for assignment help.
I. Features and filters: low-level vision
II. Grouping and fitting: mid-level vision
- Linear filters
- Edges and contours
- Binary image analysis
- Background subtraction
- Motion and optical flow
III. Multiple views
- Segmentation and clustering algorithms
- Hough transform
- Fitting lines and curves
- Robust fitting, RANSAC
- Deformable contours
- Interactive segmentation
IV. Recognition: high-level vision
- Local invariant feature detection and description
- Image transformations and alignment
- Planar homography
- Epipolar geometry and stereo
- Object instance recognition
- Object/scene/activity categorization
- Object detection
- Supervised classification algorithms
- Probabilistic models for sequence data
- Visual attributes
- Active learning
- Dimensionality reduction and manifold learning
- Non-parametric methods and big data
- Deep learning, convolutional neural networks
- Other advanced topics as time permits
TextbookSchedule (cumulative to date)
The course textbook is:
Computer Vision: Algorithms and Applications, by Rick Szeliski.
It is freely available online or may be purchased in hardcopy. Course lecture slides will be posted below and are also a useful reference.
You may also find the following books useful.
- Computer Vision: A Modern Approach, David A. Forsyth and Jean Ponce
- Computer Vision, Linda G. Shapiro and George C. Stockman
- Introductory Techniques for 3-D Computer Vision, Emanuele Trucco and Alessandro Verri.
- Multiple View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman.
- Pattern classification, Richard O. Duda, Peter E. Hart, and David G. Stork
- Pattern Recognition and Machine Learning. Christopher M. Bishop
- Visual Object Recognition. K. Grauman and B. Leibe
||Readings and links
UTCS account setup
Basic Matlab tutorial
Running Matlab at UT
See optional Latex info
|Thurs Jan 19
||Features and filters||Sec 3.1.1-2, 3.2||Linear filters
|Tues Jan 24||Sec 3.2.3, 4.2
Seam carving paper
Seam carving video
|Gradients and edges
|A0 due Friday Jan 27
See optional Latex info
|Thurs Jan 26||Sec 3.3.2-4||Binary image analysis
|Tues Jan 31||Sec 10.5
Texture synthesis by non-parametric sampling, Efros & Leung
Style transfer for video
|Thurs Feb 2||Sec 8.4 (up until 8.4.1)
|A1 due Friday Feb 3|
|Tues Feb 7||Grouping and Fitting||Sec 5.2-5.4
and clustering algorithms
|Thurs Feb 9||Sec 5.5 and skim papers
Predicting Sufficient Annotation Strength for Interactive Segmentation, Jain and Grauman, ICCV 2013 [web]
Click Carving: Segmenting Objects in Video with Point Clicks, Jain and Grauman, HCOMP 2016 [videos]
Multi-Scale Combinatorial Grouping, Arbelaez et al. CVPR 2014
Guest lecture: Suyog Jain
|Tues Feb 14||Sec 4.3.2
||Clustering wrap-up and
Hough transform, part 1
|Thurs Feb 16||Hough transform, part
|Tues Feb 21||Sec 5.1.1||Deformable contours
|Thurs Feb 23||Multiple views||Sec 4.1||Local
invariant features: detection
|A2 due Friday Feb 24|
|Tues Feb 28||Local
invariant features: description and matching
|Thurs Mar 2
||Sec 2.1.1, 2.1.2, 6.1.1, 6.1.4
|Tues Mar 7||Sec 3.6.1
HP frames video 1
HP frames video 2
|Thurs Mar 9||Midterm exam in class
1 sheet of notes is allowed
|Tues Mar 21||Sec 11.1.1, 11.2-11.5||Image warping,
stereo part 1
11 am faculty candidate talk in vision: Carl Vondrick, MIT, GDC auditorium
|Thurs Mar 23||Sec 11.1.1, 11.2-11.5
Assignments: Assignments will be given approximately every two weeks. 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. Most assignments will take significant time to complete. Please start early, and use Piazza and/or see us during office hours for help if needed. Please follow instructions in each assignment carefully regarding what to submit and how to submit it.
Extension policy: If you turn in your assignment late, expect points to be deducted. Extensions will be considered on a case-by-case basis, but in most cases they will not be granted. The greater the advance notice of a need for an extension, the greater the likelihood of leniency. For programming assignments, by default, 10 points (out of 100) will be deducted for lateness for each day late. We will use the submission program timestamp to determine time of submission. One day late = from 1 minute to 24 hours past the deadline. Two days late = from 24 hours and 1 minute to 48 hours past the deadline. We will not accept assignments more than 4 days late, or once solutions have been discussed in class, whichever is sooner.
Exams: There is an in-class midterm and a comprehensive final exam.
Participation/attendance: 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 does factor into the final grade. (See Section II of the UTCS Code of Conduct regarding attendance expectations.)
General responsibilities: Beyond the above, your responsibilities in the class are:
- Come to lecture on time.
- Check the class webpage for assignment files, notes, announcements etc.
- Use Piazza for class-related discussion and assignment help (no spoilers, please!)
- Complete the readings prior to lecture. The reading assignments listed on the schedule should be read before the associated class lecture.
- Please do not use a laptop, cell phone, tablet, etc. during class.
- Please read and follow the UTCS code of conduct.
Midterm exam: Thursday Mar 9 (in class, date tentative)
Last class meeting: Thursday May 4
Final exam: Tuesday May 16, 9-12 noon in GDC 1.304 (as set by registrar). The exam is given during the normal final exam period and will be offered at that time only.
Assignments are due about every two weeks. The dates below are tentative and are provided to help your planning. They are subject to minor shifts if the lecture plan needs to be adjusted slightly according to our pace in class.
- A0 due Fri Jan 27
- A1 due Fri Feb 3
- A2 due Fri Feb 24 (tentative)
- A3 due Fri Mar 31 (tentative)
- A4 due Fri April 14 (tentative)
- A5 due Tues May 2 (tentative)
Grades will be determined as follows. You can check your current grades online using Canvas.
- Assignments (50%, equally weighted for A1-5; 1 point for A0)
- Midterm exam (15%)
- Final exam (25%)
- Class participation, including attendance (10%)
You are encouraged to discuss the readings and concepts with classmates. However, all written work and code must be your own. All work ideas, quotes, and code fragments that originate from elsewhere must be cited according to standard academic practice.
Students caught cheating will automatically fail the course. The case will also be reported to the Office of the Dean of Students, which may institute its own disciplinary measures. If in doubt, look at the departmental guidelines and/or ask.
about Students with Disabilities
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. To determine if you qualify, please contact the Dean of Students at 471-6529; 471-4641 TTY. If they certify your needs, I will work with you to make appropriate arrangements.
about Missed Work Due to Religious Holy Days
A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.
for Assignment Write-ups (Optional)
You may use any tool for preparing assignment write-ups that you like, so long as it is organized and clear. Typically we ask for a mix of descriptions/explanations as well as embedded figures composed of images and/or plots produced in Matlab.
Below we provide some info about using Overleaf, a free online editor for Latex. Overleaf provides various Latex templates and compiles your edited .tex files into a pdf automatically. The basics:
1) go to overleaf.com
2) sign up/sign in
3) click new project on the left
4) scroll down to "Homework Assignment" and click on "more homework assignment templates"
5) choose whichever template you feel comfortable with and click "open as template"
6) start editing
7) once you are done editing, click "PDF" in the panel above. A pdf file will be generated and downloaded automatically.
Here are instructions about inserting images.
How to position images.
Captioning, scaling, resizing.