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TSBB15 Computer Vision VT2017

This course is worth 12 ECTS credits, which corresponds to approximately 320h of work per student. The time is divided among the following activities:

The course is given during the VT1 and VT2 periods. 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

We all have our offices in the B-building, ground floor, corridor A, between entrances 27 and 29.

Documents

Literature

Other topics covered in the course, such as background models are missing from these two. For side reading on other topics, we have collected pointers to relevant literature in the column "Material" in the "Lecture and Lab schedule" below.

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 Linköping 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 which is offered at three occations during 2017:
  • 2017-03-15, 14-18: Re-examination opportunity for last year's students, and voluntary mid-term examination for this years's students.
  • 2017-06-03, 14-18: Examination at end of course.
  • 2017-08-15, 14-18: Re-examination opportunity.

Old exams

The exams from 2012, 2013 and 2014 in TSBB15 can be found here.

How to use the old exams: We recommend that you use these exams as pointers into the literature ("instuderingsfrågor"). To answer the exam, read corresponding discussions in your notes, the course book, and the lecture slides. While it is faster to look at other people's exam answers, this merely gives you answers, while reading and thinking also results in understanding.

Grades

How to pass the course, and the grading criteria are described here.

Lecture and lab schedule VT1

The course content is similar to last year's course, so you may want to look there for details (e.g. slides). Updated slides for this year will be added to the table below, after each lecture.

Date,Time,Room Activity Teacher Material
Jan 18: 10.15-12
P30
Lecture 1
What is Computer Vision?
Per-Erik
Jan 19: 13.15-17
OLYM
Computer lessons 1&2
Images in Matlab and Python
Gustav
Jan 20: 10.15-12
R18
Lecture 2
Image Representations
Klas
Jan 25: 10.15-12
R23
Lecture 3
Orientation
Klas
Jan 26: 13.15-15
BL34
Lecture 4
Motion estimation, optical flow
Klas
Jan 27: 10.15-12
P30
Lecture 5
Optical flow, tracking
Klas
Feb 1: 10.15-12
BL34
Lecture 6
Clustering and learning
Per-Erik
Feb 2: 13.15-17
OLYM
Computer exercise 1
Tracking
Martin, Marcus
Feb 3: 10.15-12
BL33
Lecture 7
Overview of project 1: Tracking
Klas
Feb 10: 10.15-12
P36
Lecture 8
Local Features
Per-Erik
Feb 9: 13.15-17
OLYM
Computer exercise 2
Motion
Martin, Marcus
Feb 15: 10.15-12
R19
Lecture 9
Biological Vision
Per-Erik

Lecture and lab schedule VT2

Date,Time,Room Activity Teacher Material
Mar 20: 10.15-12
E328
Lecture 10
Recap from previous courses, ML-estimation, Gold Standard
Klas Nordberg
  • IREG: 8.2.7, 10.6, 14.1.3
  • SHB: 10.2
  • HZ: 4.7, 11.4
Mar 21: 13.15-15
BL33
Lecture 11
Calibrated geometry, PnP and the essential matrix, Robust estimation
Klas Nordberg
  • IREG: 8.5, 13.4, 14.3
  • SHB: 11.5
  • HZ: 9.6
Mar 27: 10.15-12
Moved to Mar 30
Lecture 12
RANSAC, Structure from motion, Bundle adjustment, Project 2
Klas Nordberg
Mar 28: 13.15-17
E330
Seminar 1
Presentation of project 1
Per-Erik Forssén
Mar 30: 8.15-10
BL34
Lecture 12
RANSAC, Structure from motion, Bundle adjustment, Project 2
Klas Nordberg
  • IREG: 15, 18
  • CVAA: 6.1.4, 6.1.5 (also in IREG)
Apr 4: 13.15-17
OLYM
Computer Exercise 3
Optimisation
Martin, Hannes
Apr 6: 8.15-10
BL33
NB! moved here
Lecture 13
Optimization
Per-Erik Forssén
Apr 10: 10.15-12
E330
Lecture 14
Image Enhancement
Klas Nordberg
Apr 24: 10.15-12
E330
Lecture 15
Variational Methods
Michael Felsberg
Apr 25: 13.15-17
OLYM
Computer Exercise 4
Image Restoration
Martin, Gustav
May 8: 10.15-12
BL34
Guest Lectures
Leif Haglund, Vricon
Gunnar Farnebäck, Context Vision
May 23: 13.15-17
BL33
Seminar 2
Presentation of project 2
Per-Erik Forssén

Projects

The projects are conducted in groups of 4,5 or 3 students (in order of preference).

List of project groups VT2017

  • Project 1: Tracking
    Introductory lecture on February 3
    Design plan due February 13
    Report due March 16
    Presentation seminar on March 28
  • Project 2: 3D Reconstruction
    Introductory lecture on March 27
    Design plan due April 10
    Report due May 16
    Presentation seminar on May 23

General resources

We suggest and allow you to use the following software:

  • OpenCV (Open Source Computer Vision). Version 3.1.0 will be installed on the department's Linux computers. See also the minimal example OpenCV progam (contributed by Gustav Häger).
    Read the section on OpenCV in the hints and pitfalls page. There is a cheat sheet for OpenCV. An important exception is the Background modelling with mixtures of Gaussians, which you are NOT allowed to use (as you're supposed to learn this in the course).
  • David Lowe's SIFT implementation for Matlab. This includes binary executables that are compiled into mex-code that runs from Matlab. If you don't know about Matlab mex-files ask your guide.
  • VLFeat has a a useful code library, both for Matlab and C/C++.  For example, there is an alternative implementation of SIFT here, and also an implementation of MSER here. Both are made by Andrea Vedaldi.
  • The Visual Geometry Group at Oxford University maintains code for affine invariant region detectors, produced in cooperation with other groups.
  • The Computer Vision Laboratory at ETH provides an implementation of SURF.

Project repositories

Project code should be developed under versioning control, with changes tracked according to LiU-ID of the participating group members.

  • Option A: Project groups get their repositories from GITLab at IDA Note: this is not GitHub, and GitHub should not be used.
  • Option B: On request each project group can get access to a Subversion repository, with individual login by each project member.

    Documentation for Subversion can be found here:

    If you are using a Windows based computer, we recommend that you use the Tortoise client for Subversion. We also recommend that you look at the section on Subversion in the hints and pitfalls page.

Hints and pitfalls

Based on experience from previous year's projects, we have accumulated a list of hints and pitfalls for the projects. Read them carefully before starting your project work.


Senast uppdaterad: 2017-05-16