Point distribution model: Difference between revisions

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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==

ItThe point distribution model concept has been developed by Cootes,<ref>{{citation

|author = T. F. Cootes

|title = Statistical models of appearance for computer vision

|year date=May 2004

|month = May

|url=http://www.face-rec.org/algorithms/AAM/app_models.pdf

}}</ref> Taylor ''et al.''<ref name=taylor>{{citation

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|pages = 38–59

|year = 1995

|author author1= D.H. Cooper, |author2=T.F. Cootes, |author3=C.J. Taylor and |author4=J. Graham |issue = 61

}}</ref> and became a standard in [[computer vision]] for the [[statistical shape analysis|statistical study of shape]]<ref>{{citationcite conference

|issue = 61

}}</ref> and became a standard in [[computer vision]] for the [[statistical shape analysis|statistical study of shape]]<ref>{{citation

|title = Shape discrimination in the Hippocampus using an MDL Model

|year = 2003

|conference = IMPI

|url = http://www2.wiau.man.ac.uk/caws/Conferences/10/proceedings/8/papers/133/rhhd_ipmi03%2Epdf

|author = Rhodri H. Davies and Carole J. Twining and P. Daniel Allen and Tim F. Cootes and Chris J. Taylor

|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 [[active appearance model]]s (AAM).

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|index]] 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==

First, a set of training images are manually landmarked with enough corresponding landmarks to sufficiently approximate the geometry of the original shapes. These landmarks are aligned using the [[generalized procrustes analysis]], which minimizes the least squared error between the points.

<math>k</math> aligned landmarks in two dimensions are given as

:<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 describedescribes 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:

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==Discussion==

An eigenvector, interpreted in [[euclidean space]], can be seen as a sequence of <math>k</math> 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]] worm is used as an example in the teaching of [[kernel PCA]]-based methods.

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 idea behind PDM'sPDMs is that eigenvectors can be linearly combined to create an infinity of new shape instances that will 'look like' the one in the training set. The coefficients are bounded alike the values of the corresponding eigenvalues, so as to ensure the generated 2n/3n-dimensional dot will remain into the hyper-ellipsoidal allowed ___domain—[[allowable shape ___domain]] (ASD).<ref name=taylor/>

==See also==

* [[Procrustes analysis]]

==References==

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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.

|author author1= Stegmann, M. B. and |author2=Gomez, D. D.

|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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