Syllabus for Spring 2019
Topics and Reading Assignments
Textbooks
There are no great computer vision textbooks. There are good computer vision textbooks that are somewhat old (Stockman and Shapiro, or Sonka and Hlavac). There is a new CV text by E. R. Davies that is pretty good. There is a reasonable computer vision text that is free in electronic form (Szeliski). There are also a number of reference style texts, mostly covering the software OpenCV and its various language APIs. You can access a decent OpenCV reference in the Colby Library Safari Online service. I would recommend downloading the Szeliski Book and using the OpenCV reference to get both the theoretical and practical side of computer vision.
Grading
| Projects | 50% |
| Homework | 10% |
| Mid-term Exam | 15% |
| Final Exam | 20% |
| Class Participation | 5% |
Projects and short homeworks will account for over half of your grade, with exams and class participation accounting for the remainder. The grading policy on assignments is that the tasks specified explicitly in the lab description will constitute about 85% of the assignment. If you complete the specified parts of the assignment properly, and produce a high-quality report, it's worth up to a B+ grade (26/30). In addition, the lab description will include a variety of extensions to the assignment, or you can come up with your own. Completing one or more extensions, in addition to the specified parts of the assignment, will earn you some flavor of A.
Short homework questions will be given periodically throughout the course. It will be graded in a binary manner: 1 if you hand it in by the deadline, 0 if you don't. Answers will be provided so you can check your work, and you are encouraged to ask questions.
Late Policy
The projects will tend to build upon each other, so it is critical to stay on schedule in order to be successful. Late projects will not receive credit for extensions. It is better to turn in a functional project on time than a fancy project late.
As you all have busy schedules, you may have one four-day extensions you can use at your discretion over the course of the semester, excepting only the final project. That means you may choose to hand in a project on Monday instead of Thursday for full credit.
Collaboration
Collaboration on assignment concepts and ideas between groups is acceptable, but you need to understand everything you do, and your code should be your own code. If you use ideas from another group, you must indicate that within your code or writeup. Err on the side of caution. The worst thing that can happen is the professor noticing disturbing similarities in homeworks or assignments with no attribution of credit.
Handin
For each assignment, you will put together a wiki page for the report. The report should describe the assignment, explain any required theory, and present the results. Your audience for the report is your fellow students in the course who might be interested in what you're doing.
To hand in your code, create a zip or tar file of all of the relevant source code files and place it in the private subdirectory of your Courses handin directory. If you are working with a partner or in a group, indicate where the code is located in your report.
Weekly Topics and Readings
| Week | Topics | Reading |
|---|---|---|
| 1: |
|
Lecture Notes |
| 2: |
|
Lecture Notes |
| 3: |
|
Lecture Notes |
| 4: |
|
Lecture Notes |
| 5: |
|
Durand Bilateral Filtering 2002 Lecture Notes |
| 6: |
|
Lecture Notes |
| 7: |
|
Take-home Exam Seitz Space of Stereo Images 2002 Szeliski Book Lecture Notes |
| 8: |
|
Lowe SIFT 2004 Szeliski Book Lecture Notes |
| 9: |
|
Lecture Notes |
| 10: |
|
Viola-Jones Cascade FR 2001 Wu Cascade Classifiers 2004 Pomerleau ANN Driving 1995 Rowley ANN FR 1996 Rowley ANN FR 1998 Turk Eigenfaces 1991 Murase Visual Learning 1995 Lecture Notes |
| 11: |
|
Shotton Kinect DT 2013 Deep Learning Book Lecture Notes |
| 12: |
|
Hinton Deep Learning 2006 LeCun Character Recognition 1998 He CNN OR 2015 Krizhevsky CNN OR 2014 Karpath CNN Video Labeling 2014 Vasconcelos Movie Trailers 1997 Lecture Notes |
| 13: |
|
Schodl 2000 Litwinowicz Impressionist Style 1997 Winemoller Video Abstraction 2006 Collomosse Cubism 2003 Salisbury Pen and Ink 1997 Hays Painterly Rendering 2003 Bell Product Matching 2015 Gatys Style Matching 2015 Lecture Notes |
Spring 2018