We have seen that, thanks to the marginalisation property of a Gaussian Process, any finite marginal distribution is a multivariate normal distribution. One important consequence of this is that we …
Gaussian Processes provide us with a mathematically elegant way of modelling shape deformations. As shape modelling is an application-oriented task, we are not primarily interested in mathematical elegance, but rather …
In this hands-on step, we will reproduce what we saw in the previous tutorial video and use Scalismo Lab to understand the relation between Gaussian Processes and statistical shape models …
Here we look at the relation between Gaussian Processes and point distribution models that are the statistical shape models you have been manipulating so far in Scalismo. After watching this …
In this hands-on step, we will build upon the previous tutorial video and learn how to transform a dataset of faces in correspondence into a set of discrete deformation fields …
Learn how to view a set of surfaces that are in correspondence as a set of deformation fields. This view is important as it allows us to apply the Gaussian …
In this hands-on step, you will learn how to rigidly align a dataset of misaligned faces in Scalismo. We will start by quickly defining the notion of rigid point transformation …
In the previous video we have seen that if we are given shapes in correspondence, then computing a shape model is straight-forward. However, in our exposition we were a bit …
It is very difficult to explicitly model the shape variations that define a shape family. Fortunately, we can still obtain very powerful shape models, by learning the typical deformations that …
In the previous video we have introduced Gaussian Processes and used them to model shape deformations. Gaussian Processes generalise the concept of multivariate normal distributions. Whereas the multivariate normal distribution …
The central question in shape modelling is how to model the shape variations within a shape family. In this course, the answer to this question is by means of the …
The main assumption underlying the shape models we study in this course is that the shape variations can be modelled using a normal distribution. In this article, we summarise the …
Throughout learning statistical shape modelling, we use terminology that you are unfamiliar with. This is why we are creating this comprehensive glossary of terms. A B C D E F …
In this course, you will learn how to build mathematical models that characterise the typical variations of a class of shapes. In this video we start our exploration of shape …
In this step, you will have your first interaction with the Scalismo Lab environment and shape modelling data structures. You will have the chance to reproduce the operations you’ve seen …