BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//talks.cam.ac.uk//v3//EN
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:19700329T010000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:19701025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Bayesian analysis of object data using Top Space a
nd Quotient Space models - Ian Dryden (University
of Nottingham)
DTSTART;TZID=Europe/London:20171115T094500
DTEND;TZID=Europe/London:20171115T103000
UID:TALK95080AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/95080
DESCRIPTION:The analysis of object data is becoming common\, w
here example objects under study include functions
\, curves\, shapes\, images or trees. Although the
applications can be very broad\, the common ingre
dient 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\, rotati
on and scale\, and carry out modelling and analysi
s in the resulting Quotient Space. We shall discus
s several examples\, including functional alignmen
t of growth curves via diffeomorphisms\, molecule
matching\, and 3D face regression where translatio
n and rotation are removed. Bayesian inference is
developed and the Top space versus Quotient space
approaches are compared.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:info@newton.ac.uk
END:VEVENT
END:VCALENDAR