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Scalismo Lab: posterior shape models

In this hands-on step, we will focus on part of what we have seen in the previous tutorial video and learn how to perform Gaussian Process regression and build posterior shape models.

We will start by focusing on the data required to perform regression and become familiar with ways of creating posterior Gaussian Processes in Scalismo.

We will also extend this notion of posterior models to statistical mesh models and apply them to incorporate user input, thus generating shapes with desired properties.

In the full track version, we will also focus on the importance of the observation noise in the regression task and experiment with different variations on this parameter.

To access the tutorial document:

  1. Switch to Scalismo Lab.
  2. Select the Posterior Shape Models document under:
    Documents -> Fast track, for the fast track version
    Documents -> Full track, for the full track version

If you have questions, ask them in the comments section here on FutureLearn.

If you are doing the full track version, please remember that the exercises are optional. If you find them too hard, you can continue going through the tutorial without solving the exercises.

Did anything go wrong and you have a weird shape output? Post it in our Shapes Gone Wrong Padlet!

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This article is from the free online course:

Statistical Shape Modelling: Computing the Human Anatomy

University of Basel