Recovering Perspective Pose with a Dual Step EM Algorithm
Abstract
This paper describes a new approach to extracting 3D perspective structure from 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point(cid:173) correspondence matches. Unification is realised by constructing a mixture model over the bi-partite graph representing the correspon(cid:173) dence match and by effecting optimisation using the EM algorithm. According to our EM framework the probabilities of structural cor(cid:173) respondence gate contributions to the expected likelihood function used to estimate maximum likelihood perspective pose parameters. This provides a means of rejecting structural outliers.
Cite
Text
Cross and Hancock. "Recovering Perspective Pose with a Dual Step EM Algorithm." Neural Information Processing Systems, 1997.Markdown
[Cross and Hancock. "Recovering Perspective Pose with a Dual Step EM Algorithm." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/cross1997neurips-recovering/)BibTeX
@inproceedings{cross1997neurips-recovering,
title = {{Recovering Perspective Pose with a Dual Step EM Algorithm}},
author = {Cross, Andrew D. J. and Hancock, Edwin R.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {780-786},
url = {https://mlanthology.org/neurips/1997/cross1997neurips-recovering/}
}