# Walking through contributions of principle componentsl.

### Mean shape

 The dots on the right represent the mean positions of the landmarks averaged over all the images in the model. These points mean more to us once they are joined by lines (see above screenshot). Capturing that higher meaning requires more analysis or, as in this case, human interaction (when we created the template).Note that the point models had been normalised to the same scale, same rotation and same translational position.

### Variations along the principle component axis

 To visualise sliding (walking) along an axis imagine a straight line fitted to a set of data points. Then read-out the x,y coordinates as we move back and forth along the line. Correlation between x and y (the straight line) means that the outputs are correlated. In this model we are doing a similar thing as we slide along the axis (fitted line) we read-out values in the model coordinate system that are then transformed (by the model) back into the viewing coordinates. Left, minus one standard deviation along PC1.Middle, mean shape. Right, plus one standard deviation along PC1. Movie-2012-02-15t10-48-49.flv|Movie-2012-02-15t10-23-17 First.pngSliding (walking) along the principle component axis.

### Variations along other axes

 Left, minus one standard deviation along PC1.Middle, mean shape. Right, plus one standard deviation along PC1. Movie-2012-02-15t10-48-49.flv|Movie-2012-02-15t10-23-17 First.pngSliding (walking) along the principle component axis.

### Sliding through shape space

 Left, minus one standard deviation along PC1.Middle, mean shape. Right, plus one standard deviation along PC1. Movie-2012-02-15t10-48-49.flv|Movie-2012-02-15t10-23-17 First.pngSliding (walking) along the principle component axis.