Department of Information Technology

Computer Assisted Image Analysis II

1TD398, Spring 2017, 10 credits

Goal

To give a deeper knowledge of theory and methods in image analysis, including the analysis of multidimensional images.

Contents

  • Methods for solving problems in image analysis.
  • Filtering for image enhancement and analysis.
  • Registration of images, search methods and optimisation.
  • Digital geometry.
  • Image segmentation.
  • Image-based measurements.
  • Computer vision.
  • Pattern classification and recognition.
  • Analysis of 3D images and time series.

Lectures

All lectures are given at the IT Department, Polacksbacken, according to the schedule below (TBA).

See here for the schedule.

Labs

During the lab sessions, you will be able to try out things learned during the lectures. It is beneficial to come prepared! That includes reading the material presented during the lectures, as well as the relevant sections in the book. If you come prepared, you can use the time during the lab sessions to get help from lab assistants regarding the tasks you may have difficulties with. You are required to write a lab-report describing your work on the tasks and conclusions drawn.

The three lab sessions will be in room (TBA). See labs for times and topics.

Projects

During this course, teams of 2 students each will work on a project. Each team is required to prepare a project report.
During the last session of this course, each team will have up to 10 minutes to present their work to their fellow students.

See here for more details.

Evaluation

For each lab completed before its deadline you will receive 1 additional point on the exam (the total points at the exam is 40). The same applies if the project is presented during the scheduled time. This leads to a total of 10% bonus score on the exam. Check the course's page on Studentportalen (Computer assisted image analysis II, 10 hp) where we keep track of completed labs.

At the exam you will be allowed to have one sheet (two sides) with hand-written notes. Preparing your own notes for the exam can be a helpful studying technique.

Please look through the old exams to learn what type of questions you should expect during the exam.

Literature

Most of the lectures are based on this book:

M. Sonka, V. Hlavac, and R. Boyle, "Image Processing, Analysis, and Machine Vision", 3rd edition, International Thomson Publishing, 2008. ISBN: 978-0-495-24438-7

You can get it at any bookstore (e.g. LundeQ) and also online, for example at: amazon.co.uk, adlibris.com or bokus.com.

There is also a companion book that complements the textbook. This book is not course literature, but the homepage for the book contains many demos, slides and MATLAB code that may be helpful.

Lectures notes (slides) in electronic form will be available after the lectures. Print-outs and other relevant material may be handed out during the lectures.

Updated  2016-12-18 12:27:50 by Natasa Sladoje.