## Sampling Shapes from a Shape Model

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 …

## The marginalisation property

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 …

## Scalismo Lab: Gaussian Processes and point distribution models

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 …

## Gaussian Processes and point distribution 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 …

## Scalismo Lab: from meshes to deformation fields

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 …

## Scalismo Lab: rigid alignment

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 …

## From meshes to 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 …

## Superimposing shapes

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 …

## Learning a model from example data

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 …

## Modelling shape deformations

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 …

## Gaussian Processes: from random vectors to random functions

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 multivariate normal distribution

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 …

## Statistical Shape Modelling: useful terms and concepts

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 …

## Basic notions of shape modelling

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 …

## Scalismo Lab: Hello Scalismo!

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 …