Image Understanding via Deformable Templates: From Representation to Inference
Michael I. Miller
Dept. of Electrical Engineering, Washington Univ.
high variability -
they are not rigid - but yet
they are strongly structured.
While variability comes in several forms,
it we come in all shapes and sizes.
in the understanding and analysis of shape is therefore the
of models that incorporate both structure as well
as variability in a mathematically
The global shape models introduced init Grenander's Metric Patterntheory
are intended to do this.
For this,it templatesare introduced, i.e.
it manifoldsin 1,2, and 3 dimensions.
Variability is accommodated
via the introduction
ofit transformationswhich act on the templates,
the transformations formingit groups.
In this lecture we describe progress made on representation associated with
deformable templates, as well as Bayesian inference within
the parametric structures of deformable templates.
We will show applications to automated target
recognition and the study of the
geometry of biological structures such as brains.
[Michael I. Miller,
Dept. of Electrical Engineering,
Electronic Systems and Signals Research Laboratory,
St. Louis, Missouri, 63130 USA;
Statistical Inference Problems in Computer Vision
Div. of Applied Mathematics, Brown Univ.
Low-level (Image Processing) and high-level
(Recognition) Computer Vision
problems have given rise to challenging statistical
modelling and decision issues. Conceptually, both problems
may be viewed as inference problems in Bayesian statistics
whereby: Prior distributions represent our a priori
knowledge about the world we observe, while the likelihood
functions (``data models'') describe the variability of the
observed grey-level data due to changes in lighting
conditions, noise, blur, texturing effects, occlusion,
clutter, and other degradation effects. The design of
priors models is a critical and challenging step in model
-based image analysis. In low-level problems, priors are
typically described by Markov Random Fields (MRF); in
recognition problems, priors or ``shape models'' are
required to articulate contextual constraints at multiple
levels. In this talk we will: (1) describe a promising
approach to recognition via Hierarchical/Syntactic models
(``Composition Machines'') that are closely related to
Context-Free-Grammars; the approach supports dynamic
programming-like computational algorithms; (2) describe
some interesting issues in the parameter estimation of
MRF's, due to long-range dependence and the associated
phase transitions phenomena in Statistical Mechanics.
Div. of Applied Mathematics, 182 George Street, Brown Univ., Providence, R.I. 02912 USA;