Rigid Point Registration with Expectation Conditional Maximization
This work addresses a specific computer vision problem for 3D object matching in simulated images, presenting an incremental improvement.
The paper tackles rigid 3D-to-2D point registration using maximum likelihood and Expectation Conditional Maximization, comparing two optimization algorithms for rotation and translation, with theoretical and experimental analysis of parameter estimation.
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.