Registration Via Direct Methods: A Statistical Approach
Jacques Bride (1) and Peter Meer (2)
(1) INRIA, Robotvis project
Sophia Antipolis, BP93 06902, France
(2) ECE Department
Rutgers University, Piscataway, NJ 08854-8058, USA
The ``direct methods'' achieve global image registration without
explicit knowledge of feature correspondences.
We employ the motion gradient constraint
as the relation between the motion parameters and the
measured image gradients. While this relation appears as a linear
system of equations, for any motion model (other than a translation)
we show that the underlying noise process is data-dependent, i.e.,
heteroscedastic, a fact which must be taken into account in the
parameter estimation process.
The improvement obtained using the adequate
procedure is confirmed for the 2D rigid motion model
through comparison with the traditional total least square approach.
2001 Computer Vision and Pattern Recognition Conference,
Kauai, Hawaii, December 2001, vol. I, 984-989.
Return to Research: Estimation under heteroscedasticity