# Glossary

You may find that in this course we use terminology that you are unfamiliar with. This is why we are creating this comprehensive glossary of terms.

Below you find short definitions for recurring terms in the course. The terms are ordered alphabetically. This glossary is a document on which you can collaborate by using the comment function in this step.

In case you feel you can phrase a definition for one of the terms below, please post your definition as a comment. The course educators will regularly monitor the comment section and add the best solutions to the glossary. In case there is an additional term for which you would like a definition, feel free to suggest them too. Please ‘like’ the comments you find correct and most useful.

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

### A

**Active shape model (ASM)**

*You can propose a definition/explanation for this term in the comments section.*

Introduced in:

Further reading:

### C

**Conditional distribution**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

- Conditional probability distribution - Wikipedia
- Rasmussen, C. E., Williams, C., Gaussian Processes for Machine Learning (Appendix A)
- Chuong B. Do, More on Multivariate Gaussians (Proof that the conditional distribution of a Gaussian is Gaussian)

**Confidence region**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

- Confidence region - Wikipedia
- R. Blanc et al., Confidence regions for statistical model based shape prediction from sparse observations (Advanced material)

**Correlation**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Correspondence**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Covariance matrix**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

### D

**Deformation field**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

### F

**Fitting a model**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Free-form deformation**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

### G

**Gaussian distribution**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Gaussian Process regression**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

### I

**Intensity model**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

**Iterative Closest Point (ICP) algorithm**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

### J

**Joint distribution**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

### K

**Karhunen-Loève expansion**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

- Karhunen-Loève theorem - Wikipedia
- Seeger, Matthias, Gaussian Processes in Machine Learning (Section 5.1)

**Kernel function**

### M

**Marginal distribution**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

- Marginal distribution - Wikipedia
- Rasmussen, C. E., Williams, C., Gaussian Processes for Machine Learning (Appendix A)

**Marginalisation property**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Multivariate normal distribution**

### N

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

- Multivariate normal distribution - Wikipedia
- Chuong B. Do, The Multivariate Gaussian Distribution
- Chuong B. Do, More on Multivariate Gaussians (Proofs of main properties)

### P

**Point Distribution Model**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

**Posterior model**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Principal Component Analysis (PCA)**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Prior model**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

**Procrustes Alignment**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Positive (semi) definiteness**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

- Positive definite Matrix - Wikipedia
- Rasmussen, C. E., Williams, C., Gaussian Processes for Machine Learning (Chapter 4)

### R

*Rigid registration* is the process of finding the best translation and rotation between two geometric objects. It is a process of minimizing the dissimilarity measure between the two geometric objects. The transformation matrix, which models the translation and rotation, will be applied to the geometric object being registered. Size can be filtered out if required. In this case, the registration would be called a similarity registration.

*Non-rigid registration* has the same goal as rigid registration, but seeks to find more general non-rigid transformations between the geometric objects.

Introduced in:

Further reading:

- Tam, G.K.L., et al. Registration of 3D Point Clouds and Meshes: A Survey From Rigid to Non-Rigid
- Sotiras, A., et al. Deformable medical image registration: A survey.

**Regression**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Rigid transformation**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

### S

**Sampling**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

**Shape**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

**Shape family**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

**Statistical shape model**

*You can propose a definition/explanation for this term in the comments section*

Introduced in:

Further reading:

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