AAMToolbox statistical model generator: Difference between revisions

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===Step 1) Options===
===Step 1) Options===
[[File:Stats_Model_Options_AAMToolbox.png|500px|The Model generator control panel]]<br><br>
[[File:Stats_Model_Options_AAMToolbox.png|500px|The Model generator control panel]]<br><br>
*Select the required normalisation using Procrustes method. Normally, the point model positions in the image (sideways and up-down) are not important and we '''normalise the translation. Similarly the rotation and scale'''. However, sometimes the scale is an important part of the model - for example, when analysing growing leaves. In this case scale is critical and should be unticked.<br>
*The first step in PCA is to align all the point models. In this case we assume that the images should be normalised to the''' same scale, same rotation and same translational''' position. This might not always be the case. If we want to model leaf growth then the point model should '''not''' be scaled. Alternatively, if we want to capture the pose of a face portrait the point models should not be rotationally aligned.<br>
For the '''cartoon faces we normalise for all three'''.
===Step 2) Type of model===
===Step 2) Type of model===
*Select either a shape model or shape model and a separate appearance model (AAM).
In this case the statistical '''Shape''' model will be generated from the point models alone. In other cases, we might want to include the image colours, pixel by pixel, in the PCA in which case we create two models '''Shape and Appearance''' (AAM).
**If AAM then  
**If AAM then  
***Select whether to mask out the image not within the a boundary set by the outermost landmark points
***Select whether to mask out the image not within the a boundary set by the outermost landmark points
***Select the number of pixels to be used when forming the mean image intensity (appearance).
***Select the number of pixels to be used when forming the mean image intensity (appearance).
**If modelling a subset of landmark points choose whether to find the mean position of the point models using all the landmark points (use mean positions for points not in Set) or whether to ignore landmarks not in the subset. (A subset of landmark points is selected in the AAMToolbox:Point Model Editor.)
**'''Point set'''. The AAMToolbox supports the concept of point model 'sets'. In other words, subsets of landmarks can be grouped together into a set. Set_1 is always all the points. The points that are currently selected are shown as stars. Here, all the points are selected. The options are discussed in the Tutorial on 'sets'.
**If modelling a subset of landmark points it can be useful to place the model at the mean position of all the landmarks. This can be selected by ticking 'include all points in mean'. (A subset of landmark points is selected in the AAMToolbox:Point Model Editor.)
===Step 3) Point models and images to contribute to the model
===Step 3) Point models and images to contribute to the model
*'''Select the image point models''' you want to include. Usually all of them.
*'''Select the image point models''' you want to include. Usually all of them.
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===Step 5) Exit===
===Step 5) Exit===
*The next step is to view results from the model, AAMToolbox: '''View Stats Mode'''
*The next step is to view results from the model, AAMToolbox: '''View Stats Mode'''
===Procrustes===
The first step in PCA is to align all the point models. In this case we assume that the images should be normalised to the''' same scale, same rotation and same translational''' position. This might not always be the case. If we want to model leaf growth then the point model should '''not''' be scaled. Alternatively, if we want to capture the pose of a face portrait the point models should not be rotationally aligned.
===Model Selection===
In this case the statistical '''Shape''' model will be generated from the point models alone. In other cases, we might want to include the image colours, pixel by pixel, in the PCA in which case we model '''Shape and Appearance'''.
===Options===
Verbose is useful for large Shape and Appearance models because it allows us to monitor progress. The other options control the way points are divided into sets.<br>
'''Point set'''. The AAMToolbox supports the concept of point model 'sets'. In other words, selections of points can be grouped together into a set. Set_1 is always all the points. The points that are currently selected are shown as stars. Here, all the points are selected. The options are discussed in the Tutorial on 'sets'.

Revision as of 12:40, 13 February 2012

Back to Tutorial pages

Statistical shape generator (PCA)

Generate a new PCA model from the AAMToolbox control panel

From the AAMToolbox workflow control panel elect Stats Model Generator:

Statistical Model Generator control panel.

Showing the steps (in red) to build a statistical model.
The Model generator control panel

Steps to build a shape model using principle component analysis (PCA)

Step 1) Options

The Model generator control panel

  • The first step in PCA is to align all the point models. In this case we assume that the images should be normalised to the same scale, same rotation and same translational position. This might not always be the case. If we want to model leaf growth then the point model should not be scaled. Alternatively, if we want to capture the pose of a face portrait the point models should not be rotationally aligned.

For the cartoon faces we normalise for all three.

Step 2) Type of model

In this case the statistical Shape model will be generated from the point models alone. In other cases, we might want to include the image colours, pixel by pixel, in the PCA in which case we create two models Shape and Appearance (AAM).

    • If AAM then
      • Select whether to mask out the image not within the a boundary set by the outermost landmark points
      • Select the number of pixels to be used when forming the mean image intensity (appearance).
    • Point set. The AAMToolbox supports the concept of point model 'sets'. In other words, subsets of landmarks can be grouped together into a set. Set_1 is always all the points. The points that are currently selected are shown as stars. Here, all the points are selected. The options are discussed in the Tutorial on 'sets'.
    • If modelling a subset of landmark points it can be useful to place the model at the mean position of all the landmarks. This can be selected by ticking 'include all points in mean'. (A subset of landmark points is selected in the AAMToolbox:Point Model Editor.)

===Step 3) Point models and images to contribute to the model

  • Select the image point models you want to include. Usually all of them.

The Model generator control panel

    • You will invited to give a name to the model you are about to compute

Step 4) Generate Model

Step 5) Exit

  • The next step is to view results from the model, AAMToolbox: View Stats Mode
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