- To be contacted for any question related to the course
- Cris Luengo
- To be contacted for questions related to labs and to turn in lab reports
- Kristina Lidayová
Computer Assisted Image Analysis II
1TD398, Spring 2015, 10 credits
To give a deeper knowledge of theory and methods in image analysis, including the analysis of multidimensional images.
- 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.
All lectures are given at CBA (Centre for Image Analysis), Polacksbacken room 2115 (building 2, 1st floor). Please come on time!
During the lab sessions, you will be able to try out things learned in the lectures. Please come prepared! That means, go over the lectures again and read the relevant sections in the book. If you are properly prepared, you should be able to finish all the exercises during the lab session. You will be required to write a report describing exercises you did not finish during the lab session.
The three lab sessions will be in room 1313D (building 1, 3rd floor). See the course schedule for times and topics.
During this course, teams of 2 students each will work on a project. During the last session of this course, each team will have 10 minutes at most to present their work to their fellow students.
For each lab finished before its deadline you will receive 1 additional point on the exam (the exam will have a total of 40 points). 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.
On the exam you will be allowed to carry one sheet (two sides) with hand-written notes. Preparing your own notes for the exam is a helpful studying technique, and we see no point in memorizing things.
Please look through the old exams to learn what type of questions you should expect during the exam.
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
There is also a companion book that complements the textbook. This is book is not course literature but the homepage for the book contains many demos, slides and MATLAB code that may be helpful.
Copies of slides and other material might be handed out during the lectures.