TSBB15 2012 (last year's course)

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

• Lectures, 16 × 2h = 32h
• Computer lessons, 2 x 2h = 4h
• Computer assignments, 4 x 4h = 16h
• Written examination, 4h
• Own studies, approx. 108h
• Project work, approx. 156h
  

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

• Per-Erik Forssén, lectures, examiner
• Klas Nordberg, lectures
• Michael Felsberg, lectures

• Kristoffer Öfjäll, lessons, computer exercises
• Vasileios Zografos, computer exercises, project guide
  
• Liam Ellis, computer exercises, project guide
  
• Erik Ringaby, project guide
  
• Johan Hedborg, computer exercise, project guide
  

Documents

• Course description in Studiehandboken (the LiTH study guide)
  
• Course schedule is available here (now both VT1 and VT2)

Literature

The course is based on the book

• Sonka, Hlavac, Boyle, Image Processing, Analysis, and Machine Vision, 3rd edition, Thomson (2008).

There is a student (paperback) edition of this book (labeled ISE) from Nelson Engineering.

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

• Mar 6, 14-18: Re-examination opportunity for last year's students, and voluntary mid-term examination for this years's students.
• Jun 2, 14-18: Examination at end of course.
  
• Aug 14, 14-18: Re-examination opportunity.

Old exams

Old exams can be found here. These are from the previous courses TS1017, and TSBB12, which covered basically the same theory as this course.

Grades

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

Lecture schedule VT1

The course content is similar to last year's course TS1017, so you may want to look there for details (e.g. slides).
Updated slides will appear in the table below, after each lecture.

• SHB = Sonka, Hlavac and Boyle "Image Processing, Analysis, and Machine Vision"
• GW = Gonzalez and Woods, "Digital Image Processing"
• HZ = Hartley and Zisserman, "Multiple View Geometry in Computer Vision" (2nd edition 2003). Available as e-book from LiU library
• Wikipedia = Search wikipedia for the listed term
• Mathworld = Search http://mathworld.wolfram.com/ for the listed term
• Edupack - orientation is a tutorial found here
• EDUPACK2 = www2.cvl.isy.liu.se/Education/Edupack/orientation2.pdf
• RNDF = J. Weickert: A Review of Nonlinear Diffusion filtering, Scale-Space 1997

Lecture schedule VT2

List of lectures. 

Computer lessons VT1

The material for both computer lessons is here.

Computer exercises VT1

Links to material for the exercises are in the table below.

Computer exercises VT2

More info here soon.

Projects

The projects are conducted in groups of typically 3-4 students. No group can have more than 4 members.

Groups VT 2012

• Project 1: Tracking
  Introductory lecture on Feb 8
  Design plan due Feb 15
  Report due March 26
  Presentation seminar on April 17

• Project 2: 3D reconstruction
  Introductory lecture on April 18
  Design plan due April 25
  Presentation seminar on May 15
  Report due May 18

General resources

We suggest and allow you to use the following software

• OpenCV (Open Source Computer Vision).  Version 2.3.1 will be installed on the department's Linux computers.
  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.

Subversion

Each project will have access to a part of a Subversion repository, with individual login by each project member. User names and passwords are managed according to (use the first that applies)

• If you already are a user of ISY Subversion, use the same name and login as before
• If you have an active LiU student account, use the account name for logging in. A password will be created and distributed at the beginning of the first project.
• If none of the above applies, a temporary user name will be crated and distributed at the beginning of the first project. A password will be created and distributed at the beginning of the first project.

In the case that a new password is created and distributed at the beginning of the first project, this password is only temporary and must be changed to new one before the account is actively used.  This is done by following the instructions here

• https://svn.isy.liu.se

The addresses to the Subversion repositories will be:

• https://svn.isy.liu.se/student2012/tsbb15/G1 to https://svn.isy.liu.se/student2012/tsbb15/G6
  

• https://svn.isy.liu.se

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 accumuated a list of hints and pitfalls for the projects here.  Read them carefully before starting your project work.