arXiv Open Access 2018

Rigid Point Registration with Expectation Conditional Maximization

Jing Wu

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.CV

Penulis (1)

Jing Wu

Format Sitasi

APA MLA BibTeX

Wu, J. (2018). Rigid Point Registration with Expectation Conditional Maximization. https://arxiv.org/abs/1803.02518

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓