  This paper addresses the issue of matching rigid 3D object points with 2D
image points through point registration based on maximum likelihood principle
in computer simulated images. Perspective projection is necessary when
transforming 3D coordinate into 2D. The problem then recasts into a missing
data framework where unknown correspondences are handled via mixture models.
Adopting the Expectation Conditional Maximization for Point Registration
(ECMPR), two different rotation and translation optimization algorithms are
compared in this paper. We analyze in detail the associated consequences in
terms of estimation of the registration parameters theoretically and
experimentally.
