It is our strong belief that the only way to learn image analysis is to try the algorithms on real images with a computer. Therefore, a number of computer exercises are included in this course. They are not compulsory, but it is highly recommended that you do them.
Each exercise will have a number of scheduled class hours in a Computer Room. The exercises (except for the first one) will contain quite a lot of programming when implementing the algorithms and your ideas.
Of course, if you have questions regarding the CEs or the m-files, don't hesitate to send me an email.
Back to Statistical Image Analysis homepage.
This exercise is for you who have none or little experience using Matlab with images.Document:
Read through the chapter regarding Pattern Recognition in the Lecture Notes (IASS) before you begin with this exercise.
The Data used in this exercise can be found here.
Here is the half-done Matlab file for estimating the error rates:
Here's the last CE, which deals with simulation of noise and Markov Random Fields.Document:
Here is an outline of the main program. You may need the function neighbours.m from CE2. Probably, some adjustments have to be made, though.Demonstration
Here's the files for the Potts model experiment in Section 3.4 in the CE.