Shape Modelling Using Maximum Autocorrelation Factors

Rasmus Larsen

AbstractThis paper addresses the problems of generating a low dimensional representation
of the shape variation present in a training set after alignment using
Procrustes analysis and projection into shape tangent space.
We will extend the use of principal components analysis in the original
formulation of Active Shape Models by Timothy Cootes and Christopher Taylor by
building new information into the model.
This new information consists of two types of
prior knowledge. First, in many situation we will be given an ordering of the
shapes of the training set. This situation occurs when the shapes of the
training set are in reality a time series, e.g.\ snapshots of a beating heart
during the cardiac cycle or when the shapes are slices of a 3D structure, e.g.
the spinal cord. Second, in almost all applications a natural order of the
landmark points along the contour of the shape is introduced. Both these types
of knowledge may be used to defined Shape Maximum Autocorrelation
The resulting point distribution models are compared to
ordinary principal components analysis using leave-one-out validation.
TypeConference paper [With referee]
ConferenceProceedings of the Scandinavian Image Analysis Conference (SCIA'01)
EditorsIvar Austvoll
Year2001    Month June    pp. 98-103
AddressBergen, Norway
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics