
In examplebased pose estimation, the configuration or "pose" of an evolving object is sought given visual evidence, having to rely uniquely on a
set of examples. We assume here that, in a training stage, a number of feature measurements is extracted from the available images,
while an "oracle" provides us with the true object pose at each instant.
In this scenario, a sensible approach consists in learning maps
from features to poses, using the information provided by the training set. In particular, multivalued mappings linking feature values to set
of training poses can be easily constructed. A probability measure on any feature space is then naturally mapped to a convex set of probabilities
on the set of training poses, in a form of a "belief function". Given a test image, its feature measurements translate into a collection of belief
functions on the set of training poses, which when combined yield there an entire family of probability distributions. From the latter, both a single,
central pose estimate and a set of extremal estimates can be computed, together with a measure of how reliable the estimate is. Measuring the
conflict among the inferred belief functions provides a way of tackling occlusions and imperfect localization, and flags the need to update the
model.
We call this technique "Belief Modelling Regression".


Relevant papers:



Fabio Cuzzolin and Ruggero Frezza Evidential modeling for pose estimation
Proceedings of the 4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA'05)
Carnegie Mellon University, Pittsburgh, July 2005 

