**Next:**Content-based
Image Retrieval
**Up:**Computer
Vision
**Previous:**Interactive
Model Recognition from
###
Pose Estimation: Known Objects
in Unstructured Environments

*F. Callari, G. Soucy, F.P. Ferrie (McGill), D. Baird, D. Lamb (Hymarc
Ltd.)*
This project focuses on investigating methods for pose and point estimation
(PPE) on complex, rigid, known, non-cooperative 3-D objects from range
images. The ``point'' estimation issue is remarked and studied alongside
the ``pose'' estimation one (in itself a classic problem of computer vision),
in that it is the goal of the project to build a system able to make confident
assertions about the position in 3-D space of particular ``relevant'' points
on the objects at hand. This goal stems from one possible application of
this research, namely manipulation and docking operations of space station
structures in space. Our proposed solution is a Bayesian inference scheme,
based on several layers of information processing. Starting from range
images of the objects, a chain of shape analysis processes produces first
a segmentation of large, reliable features on the objects, whose shape
is modelled by geometric primitives (superellipsoids in the proposed test-bed
solution). The shape parameters are used for part recognition (via a Bayesian
discriminative classifier) and for placing ``landmark'' points upon the
parts themselves. Eventually, the landmarks are used to estimate the target
point position. Uncertainty in the modelization process is carried along,
and expressed in the form of posterior probability distributions about
the target. This effectively quantifies the confidence degree in the estimation.
Extensive tests of the above sketched technique on test objects have given
promising results about the practical feasibility of the proposed methodology.
More details are available on line:

http://www.cim.mcgill.ca/~
apl/Papers/callari-landmark-identification.ps.gz

*Annual Report*

*Fri Nov 26 23:00:32 GMT 1999*