Computer Vision Laboratory (CVL)

This course is worth 6 ECTS credits, which corresponds to approximately 160h of work per student. The time is divided among the following activities:

Lectures	20h (10x2h)
Written examination	4h
Seminars	8h (2x4h)
Own studies	52h (approx.)
Project work	76h (approx.)

The course is given during the HT1 period, and will run in "distance mode". Lectures will be made available as pre-recorded videos that can be accessed through the Lisam course page. Scheduled lecture slots will be used as Q&A sessions over Zoom. Note that not all the time slots in the TimeEdit schedule will be used. A few spare slots have been reserved in case a session has to be moved. The actually planned lectures are listed below on this page, and any changes to this plan will be announced over email, during the lectures, and on this page.

People

• Per-Erik Forssén, lectures, examiner
• Michael Felsberg, lectures
• Felix Järemo Lawin, supervisor project 1
• Linbo He, supervisor project 2
• Abdelrahman Eldesokey, supervisor project 1 and 2

Our offices are in the B-building. Lecturers sit in corridor D, first floor between entrances 25 and 27. Project supervisors sit in Visionen.

Documents

• Detailed information and documents related to the two projects will be provided through the course repository hosted in LiUs GitLab (accessible to anyone with a LiU login).
  
• Course description in Studieinfo (the web interface to Bilda)
  

Literature

• Goodfellow et al., Deep Learning, MIT Press (2016).
  The book is available as full pdf. See also Book webpage.
• Richard Szeliski, Computer Vision: Algorithms and Applications, Springer Verlag (2011).
  The book is available as an on campus e-book via the LiU library. See also Book webpage.

Registration

If you intend to take the course but are not registered, make sure to register ASAP, using the Student portal. You need to be registered on the course to receive course email, and to have results input to Ladok. If you take the course but are not registered to any program at the University, please contact the course examiner in order to make sure that you receive email about the course.

Examination

• The course has a written examination in English. This year, the exam will be a "distance exam", which is accessed, and handed in using the Submission tab on the Lisam page for the course. The exam has the same format as previous years. See examples below:
• Example exam from 2017-10-17 [pdf]
• Example solutions from 2018-01-02 [pdf]
• The reexamination, 2021-01-04, 8-12, will be a distance exam.
  Students registered for the exam will be able to access the exam under the "Submissions" tab on the Lisam page for the course.
• The projects are examined using written reports and oral presentations in English.

Lecture schedule 2020

During HT1 2020 the lectures are given in the form of pre-recorded videos, with a Q&A session at the scheduled lecture time (a dedicated time when you can ask questions about the content). For best use of your time, you should thus watch the lecture before the time in the schedule, and note down your questions. The pre-recorded lectures can be accessed through the Lisam page for the course.

Before the lectures, the lecture slides from last year can be found in the course repository. Updated slides will be pushed after the video content has been produced. The repository also contains additional relevant literature for side reading.

Date,Time	Activity	Teacher
August 31: 08.15-10 Q&A starts 9.15	Lecture 1 Q&A Introduction	Per-Erik Forssén
September 1: 10.15-11	Lecture 2 Q&A Feature Descriptors and Evaluation	Per-Erik Forssén
September 2: 13.15-14	Lecture 3 Q&A Convolutional Neural Networks: Introduction and Theory	Michael Felsberg
September 4: 9.00-9.45 Q&A starts 9.00	Lecture 4 Q&A Image Classification with Convolutional Neural Networks	Michael Felsberg
September 8: 10.15-12	Lecture 5 Q&A Compound Descriptors and Metric Learning	Per-Erik Forssén
September 9: 13.15-15	Lecture 6 Project 1: Visual Object Recognition	Per-Erik Forssén
September 15: 10-12 Q&A starts 10.15	Lecture 7 Q&A Visual Object Detection	Per-Erik Forssén
September 16: 13-15 Q&A starts 14.00	Lecture 8 Q&A Visual Object Tracking: Introduction	Michael Felsberg
September 22: 10.15-12 Q&A starts 10.45	Lecture 9 Q&A Discriminative Correlation Filters for Visual Tracking	Michael Felsberg
September 23: 13.15-15	Seminar 1 Presentation of project 1	Per-Erik Forssén
September 23: 15.15-17	Lecture 10 Project 2: Visual Object Tracking	Per-Erik Forssén
October 14: 13.15-15	Seminar 2 Presentation of project 2	Per-Erik Forssén

A more extensive schedule can be found in TimeEdit. It also contains scheduled project time ("Projekttid", when you have exclusive access to the computer room Olympen) as well as backup lectures. Note that teachers will only be present at activities listed in the table above.

Projects