CPSC 425 Computer Vision - Winter 2019/20

Term 1 (September – December, 2019)

Course Description

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.

People

Instructor: Jim Little (little [at] cs.ubc.ca).
Office hours:
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

Topics

Here is a rough schedule and tentative list of topics and readings (subject to change).

Important Dates

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

Lectures

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. 16 Midterm
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

Discussion

Piazza group - Please use this link to enroll yourself.

Assignments

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

Exams

There will be one midterm and one final exam (see important dates).
The midterm is closed-book. The final exam is also closed book.

Grading Scheme

In-class (clicker questions): 5%
Assignments: 25%
Midterm exam: 25%
Final exam: 45%

The instructor reserves the right to change this scheme (but does not anticipate using that right).

Textbook

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:

Interesting Links

Interesting links

Course Policies