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The '''point distribution model''' is a model for representing the mean geometry of a shape and some statistical modes of geometric variation inferred from a training set of shapes.
==Background== The |author = T. F. Cootes
|title = Statistical models of appearance for computer vision
|
}}</ref> Taylor ''et al.''<ref name=taylor>{{citation▼
▲|url=http://www.isbe.man.ac.uk/~bim/Models/app_models.pdf
▲}}</ref> Taylor ''et al''<ref name=taylor>{{citation
|authors = D.H. Cooper, T.F. Cootes, C.J. Taylor and J. Graham▼
|title = Active shape models—their training and application
|journal = Computer Vision and Image Understanding
|pages = 38–59
|year = 1995
▲|
}}</ref> and became a standard in [[computer vision]] for the [[statistical shape analysis|statistical study of shape]]<ref>{{
|title = Shape discrimination in the Hippocampus using an MDL Model
|authors = Rhodri H. Davies and Carole J. Twining and P. Daniel Allen and Tim F. Cootes and Chris J. Taylor▼
|year = 2003
|conference = IMPI
|url = http://www2.wiau.man.ac.uk/caws/Conferences/10/proceedings/8/papers/133/rhhd_ipmi03%2Epdf
▲|
}}</ref> and for [[image segmentation|segmentation]] of [[medical imaging|medical images]]<ref name=taylor/> where shape priors really help interpretation of noisy and low-contrasted [[pixel]]s/[[voxel]]s. The latter point leads to [[active shape model]]s (ASM) and [[Active Appearance Model]]s (AAM).▼
|access-date = 2007-07-27
|archive-url = https://web.archive.org/web/20081008194350/http://www2.wiau.man.ac.uk/caws/Conferences/10/proceedings/8/papers/133/rhhd_ipmi03%2Epdf
|archive-date = 2008-10-08
|url-status = dead
▲}}</ref> and for [[image segmentation|segmentation]] of [[medical imaging|medical images]]<ref name=taylor/> where shape priors really help interpretation of noisy and low-contrasted [[pixel]]s/[[voxel]]s. The latter point leads to [[active shape model]]s (ASM) and [[
Point distribution models rely on [[landmark point]]s. A landmark is an annotating point posed by an anatomist onto a given locus for every shape instance across the training set population. For instance, the same landmark will designate the tip of the [[index finger]] in a training set of 2D hands outlines. [[Principal component analysis]] (PCA), for instance, is a relevant tool for studying correlations of movement between groups of landmarks among the training set population. Typically, it might detect that all the landmarks located along the same finger move exactly together across the training set examples showing different finger spacing for a flat-posed hands collection.
==Details==
<math>k</math> aligned landmarks in two dimensions are given as
An eigenvector, interpreted in [[euclidean space]], can be seen as a sequence of n euclidean vectors associated to corresponding landmark and designating a compound move for the whole shape. Global nonlinear variation is usually well handled provided nonlinear variation is kept to a reasonable level. Typically, a twisting [[nematode worm]] is used as an example in the teaching of [[kernel PCA]]-based methods.▼
:<math>\mathbf{X} = (x_1, y_1, \ldots, x_k, y_k)</math>.
It's important to note that each landmark <math>i \in \lbrace 1, \ldots k \rbrace </math> should represent the same anatomical ___location. For example, landmark #3, <math>(x_3, y_3)</math> might represent the tip of the ring finger across all training images.
Now the shape outlines are reduced to sequences of <math>k</math> landmarks, so that a given training shape is defined as the vector <math>\mathbf{X} \in \mathbb{R}^{2k}</math>. Assuming the scattering is [[gaussian distribution|gaussian]] in this space, PCA is used to compute normalized [[eigenvectors]] and [[eigenvalues]] of the [[covariance matrix]] across all training shapes. The matrix of the top <math>d</math> eigenvectors is given as <math>\mathbf{P} \in \mathbb{R}^{2k \times d}</math>, and each eigenvector describes a principal mode of variation along the set.
Finally, a [[linear combination]] of the eigenvectors is used to define a new shape <math>\mathbf{X}'</math>, mathematically defined as:
:<math>\mathbf{X}' = \overline{\mathbf{X}} + \mathbf{P} \mathbf{b}</math>
where <math>\overline{\mathbf{X}}</math> is defined as the mean shape across all training images, and <math>\mathbf{b}</math> is a vector of scaling values for each principal component. Therefore, by modifying the variable <math>\mathbf{b}</math> an infinite number of shapes can be defined. To ensure that the new shapes are all within the variation seen in the training set, it is common to only allow each element of <math>\mathbf{b}</math> to be within <math>\pm</math>3 standard deviations, where the standard deviation of a given principal component is defined as the square root of its corresponding eigenvalue.
PDM's can be extended to any arbitrary number of dimensions, but are typically used in 2D image and 3D volume applications (where each landmark point is <math>\mathbb{R}^2</math> or <math>\mathbb{R}^3</math>).
==Discussion==
▲An eigenvector, interpreted in [[euclidean space]], can be seen as a sequence of
Due to the PCA properties: eigenvectors are mutually [[orthogonal]], form a basis of the training set cloud in the shape space, and cross at the 0 in this space, which represents the mean shape. Also, PCA is a traditional way of fitting a closed ellipsoid to a Gaussian cloud of points (whatever their dimension): this suggests the concept of bounded variation.
The
==See also==▼
* [[Procrustes analysis]]▼
==References==
<references/>
<!-- Not referenced [4]{{citation
|authors = Stegmann, M. B. and Gomez, D. D.▼
|title = A Brief Introduction to Statistical Shape Analysis
|year = 2002
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|url=http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=403
|quote=Images, annotations and data reports are placed in the enclosed zip-file.
|name-list-style=amp }} -->
▲==See also==
▲* [[Procrustes analysis]]
==External links==
* [https://web.archive.org/web/20080509041813/http://www.isbe.man.ac.uk/~bim/Models/index.html Flexible Models for Computer Vision], Tim Cootes, Manchester University.
* [http://www.icaen.uiowa.edu/~dip/LECTURE/Understanding3.html A practical introduction to PDM and ASMs].
[[Category:Computer vision]]
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