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SUMMARY:Bayesian analysis of object data using Top Space and Quotient Spac
 e models - Ian Dryden (University of Nottingham)
DTSTART:20171115T094500Z
DTEND:20171115T103000Z
UID:TALK95080@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:The analysis of object data is becoming common\, where example
  objects under study include functions\, curves\, shapes\, images or trees
 . Although the applications can be very broad\, the common ingredient in a
 ll the studies is the need to deal with geometrical invariances. For the s
 imple example of landmark shapes\, one can specify a model for the landmar
 k co-ordinates (in the Top Space) and then consider the marginal distribut
 ion of shape after integrating out the invariance transformations of trans
 lation\, rotation and scale.  An alternative approach is to optimize over 
 translation\, rotation and scale\, and carry out modelling and analysis in
  the resulting Quotient Space. We shall discuss several examples\, includi
 ng functional alignment of growth curves via diffeomorphisms\, molecule ma
 tching\, and 3D face regression where translation and rotation are removed
 . Bayesian inference is developed and the Top space versus Quotient space 
 approaches are compared.
LOCATION:Seminar Room 1\, Newton Institute
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