Good.

Anything else?

So we are not switching the exercise.

The doodle poll seemed to be nonconclusive.

So the two best options would be either the option we already have or a different one.

And we are going to keep the option that we already have because some people have already

scheduled their calendar according to this schedule.

So I thought the better option is to keep what we already have instead of switching.

Okay.

Good.

So if you decide to go for the early exercise you might have very good supervision because

only few people are there.

It's Wednesday at 9?

Not 8?

It's in the universe.

So you can look it up.

It won't be changed.

Yeah.

Okay.

Good.

So today we are going to do a refresher course and we will talk about a tool that is called

singular value decomposition.

And as you will see this is a very powerful tool and we use it in image processing all

the time because it allows us to solve linear problems quite efficiently and quite robustly.

And yeah, we will talk about that today.

Okay.

So should we wait a bit?

Okay.

Okay.

So let's start.

Welcome back to medical image processing, interventional medical image processing.

Okay.

There are still people coming in but have a seat.

Don't worry.

This is a refresher course anyway.

So today we will talk about singular value decomposition and there is a certain rumor

that is between the students every semester.

There is this rumor that if you are in the oral exam and you are not exactly aiming by

asking very strange questions, then probably the answer is SVD because probably there is

something linear in the topic that we are discussing about and then SVD might be a good

option to solve the entire problem.

Good.

So what is SVD?

SVD is a powerful tool.

It's a matrix decomposition but first let's reconsider what matrices are and what we are

doing, what we might use the SVD for.

So let's say we have a matrix.

So we start from the very basics and of course we will then go into more detail.

But let's consider what a matrix is.

Typically you have a matrix and you call the matrix probably A. You will realize that this