Computer vision, broadly speaking, is a research field aimed to enable computers to process and interpret visual data, as sighted humans can. It is one of the most exciting areas of research in computing science and among the fastest growing technologies in today’s industry. This course provides an introduction to the fundamental principles and applications of computer vision (see topics).
Prerequisite: All of MATH 200, MATH 221 and either (a) CPSC 221 or (b) all of CPSC 260, EECE 320.
Instructor: Jim Little (little [at] cs.ubc.ca).
Thursday Dec 12 1-2pm ICCS 117
Friday Dec 13 2-330pm ICCS 117
or by appt.
NOTE: no office hours the week of Dec 2 - use Piazza
Office hours for the week of Dec 9
Thu Dec 12 1-2pm ICCS 117
Fri Dec 13 130pm-3pm ICCS 183 (NOTE LOCATION)
or by appt.
TA: Alex Fan (fan [at] cs.ubc.ca). Office hours: Friday Dec 13 9-1155 X237
TA: Farnoosh Javadi (fjavadi [at] cs.ubc.ca). Office hours: Wed Dec 11 (10-11) X241; Thu Dec 12 (10-11) X239
Here is a rough schedule and tentative list of topics and readings (subject to change).
|First Day of Classes||4 September (Wednesday)|
|Add/Drop Deadline||17 September (Tuesday)|
|Drop with W Deadline||11 October (Friday)|
|Midterm Exam||16 October (Wednesday) in class|
|Last Day of Classes||29 November (Friday)|
|Final Exam||December 16, 1200pm DMP 310 but consult exam for official location/time|
Mondays, Wednesdays and Fridays, 4:00pm-450pm, DMP ???
We will post the lecture materials here.
|Sep. 4||Introduction||Lecture 1|
|Sep. 6||Image Formation||Lecture 2|
|Sep. 9||Image Formation||Lecture 3|
|Sep. 11||Image Filtering||Lecture 4|
|Sep. 13||Image Filtering||Lecture 5|
|Sep. 16||Image Filtering||Lecture 6|
|Sep. 18||Sampling||Lecture 7|
|Sep. 20||Sampling||Lecture 8||template matching|
|Sep. 23||Scaled Representations||Lecture 9|
|Sep. 25||Local Image Features||Lecture 10|
|Sep. 27||Local Image Features||Lecture 11|
|Sep. 30||Corners||Lecture 12|
|Oct. 2||Corners, Texture intro||Lecture 13 (updated 191008)|
|Oct. 4||Texture||Lecture 14|
|Oct. 7||Texture||Lecture 15|
|Oct. 9||Colour||Lecture 16|
|Oct. 11||Midterm review||Lecture 17 Midterm review|
|Oct. 18||Local Image Features SIFT||Lecture 18 SIFT|
|Oct. 21||Local Image Features SIFT||Lecture 19 SIFT and others|
|Oct. 23||Model Fitting||Lecture 20|
|Oct. 25||Model Fitting||Lecture 21|
|Oct. 28||Model Fitting||Lecture 22|
|Oct. 30||Stereo||Lecture 23|
|Nov. 1||Stereo||Lecture 24||Lecture 25|
|Nov. 4||Optical Flow||Lecture 25|
|Nov. 6||Optical Flow and Grouping||Lecture 26 Optical Flow (cont.)||Lecture 27 Clustering (Grouping)|
|Nov. 8||Classification||Lecture 28 Classification|
|Nov. 13||Scene Classification||Lecture 28 Classification||Lecture 29 Scene Classification|
|Nov. 15||Classification||Lecture 29 Scene Classification|
|Nov. 18||Object Detection||Lecture 30 Object Detection|
|Nov. 20||Object Detection||Lecture 31|
|Nov. 22||Object Detection, Grouping||Lecture 32|
|Nov. 25||Grouping, NNs||Lecture 33|
|Nov. 27||CNNs||Lecture 34||Reading: ConvNet tutorial|
|Nov. 29||Final Review||Lecture 35|
There are SIX (had planned seven) assignments given throughout the term. The first is a self-study tutorial introduction to Python for computer vision (that is not marked). The other five are handed in to be marked. Each assignment has a specific due date and time which will be announced here (a tentative schedule is posted).
|Assignment 0||No due date (but try to complete it by Sep. 11)|
|Assignment 1||Due 1159pm Tuesday Sep. 24|
|Assignment 2||Due 1159pm Tuesday Oct. 8|
|Assignment 3||Due 1159pm Thursday Oct. 24|
|Assignment 4||Due 1159pm Tuesday Nov. 5|
|Assignment 5||Due 1159pm Friday Nov. 29|
There will be one midterm and one final exam (see important dates).
The midterm is closed-book. The final exam is also closed book.
In-class (clicker questions): 5%
Midterm exam: 25%
Final exam: 45%
The instructor reserves the right to change this scheme (but does not anticipate using that right).
The course uses the following textbook, which is recommended (but not required):
Another useful textbook (which can be downloaded from http://szeliski.org/Book/) is:
Here is another one which can also be freely downloaded as a PDF from SpringeLink, through UBC Library (must login using CWL).
The following textbooks are also on reserve in the reading room: