Datorseende (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 HT period. Note that not all the time slots in the course schedule will be used. A few spare slots have been reserved in case a lecture has to be moved. The actually planned lectures are listed below on this page, and any changes to this plan will be announced during the lectures, and on this page.

People (preliminary)

• 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

We all have our offices in the B-building, corridor D, first floor between entrances 25 and 27.

Documents

• Detailed information and documents related to the two projects will be provided through the course repository hosted in IDAs 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. The exam has the same format as last year. See examples below:
• Example exam from 2017-10-17 [pdf]
• Example solutions from 2018-01-02 [pdf]
• The projects are examined using written reports and oral presentations in English.

Lecture schedule 2019

The lecture slides from last year can be found in the course repository before the lectures. Updated slides will be pushed after each lecture. The repository also contains additional relevant literature for side reading. All lectures and seminars will take place in the room Systemet at ISY.

Date,Time	Activity	Teacher
September 2: 08.15-10	Lecture 1 Introduction	Per-Erik Forssén
September 3: 10.15-12	Lecture 2 Feature Descriptors	Per-Erik Forssén
September 4: 13.15-15	Lecture 3 Convolutional Neural Networks: Introduction and Theory	Michael Felsberg
September 6: 8.15-10	Lecture 4 Image Classification with Convolutional Neural Networks	Michael Felsberg
September 10: 10.15-12	Lecture 5 Compound Descriptors and Metrics	Per-Erik Forssén
September 11: 13.15-15	Lecture 6 Project 1: Visual Object Recognition	Per-Erik Forssén
September 13: 08.15-10	Lecture 7 Visual Object Detection	Per-Erik Forssén
September 16: 08.15-10	Lecture 8 Visual Object Tracking: Introduction	Michael Felsberg
September 17: 10.15-12	Lecture 9 Discriminative Correlation Filters for Visual Tracking	Michael Felsberg
September 25: 13.15-15	Seminar 1 Presentation of project 1	Per-Erik Forssén
September 25: 15.15-17	Lecture 10 Project 2: Visual Object Tracking	Per-Erik Forssén
October 16: 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 (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