Generalized Procrustes analysis
 | It has been suggested that this article be merged into Procrustes analysis. (Discuss) Proposed since June 2008. |
The Procrustes distance provides a metric to minimize in order to align a pair of shape instances annotated by landmark points. Generalized Procrustes analysis (GPA) is a procedure applying the aforementioned Procrustes analysis method to align a population of shapes instead of only two shape instances.
GPA This is one of the methods achieving this goal, namely useful to build a Point Distribution Model or to undertake any shape study on the training set. The algorithm outline is the following:
- 1: choose a reference shape among the training set instances
- 2: align all other instances on current reference
- 3: compute the mean shape of the current training set
- 4: if the proscrustes distance between the mean shape and the reference is above a threshold, set reference to mean shape and continue to step 2.
References
- J.F. Meullenet, R. Xiong, and C.J. Findlay (2007). Multivariate and Probabilistic Analyses of Sensory Science Problems. IFT Press & Blackwell Publishing. ISBN 0813801780.
{{cite book}}: CS1 maint: multiple names: authors list (link)
- I.L. Dryden and K.V. Mardia (1998). Statistical Shape Analysis. John Wiley & Sons. ISBN 0471958166.