Bayesian analysis of object data using Top Space and Quotient Space models

Add to your list(s) Download to your calendar using vCal

• Ian Dryden (University of Nottingham)
• Wednesday 15 November 2017, 09:45-10:30
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

GFSW03 - Shape analysis and computational anatomy

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 all the studies is the need to deal with geometrical invariances. For the simple example of landmark shapes, one can specify a model for the landmark co-ordinates (in the Top Space) and then consider the marginal distribution of shape after integrating out the invariance transformations of translation, 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, including functional alignment of growth curves via diffeomorphisms, molecule matching, and 3D face regression where translation and rotation are removed. Bayesian inference is developed and the Top space versus Quotient space approaches are compared.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• bld31

Note that ex-directory lists are not shown.