Detection of multiple meaningful primitive geometric models

Radwa Fathalla and George Vogiatzis

In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.123

Abstract

This paper presents a novel approach to the computation of primitive geometrical structures, where no prior knowledge about the visual scene is available and a high level of noise is expected. We based our work on the grouping principles of proximity and similarity, of points and preliminary models. The former was realized using Minimum Spanning Trees (MST), on which we apply a stable alignment and goodness of fit criteria. As for the latter, we used spectral clustering of preliminary models. The algorithm can be generalized to various model fitting settings, without tuning of run parameters. Experiments demonstrate the significant improvement in the localization accuracy of models in plane, homography and motion segmentation examples.

Session

Poster Session

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Paper (PDF, 12 pages, 4.1M)

Bibtex File

Citation

Radwa Fathalla, and George Vogiatzis. Detection of multiple meaningful primitive geometric models. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.

BibTex

@inproceedings{BMVC.28.123
	title = {Detection of multiple meaningful primitive geometric models},
	author = {Fathalla, Radwa and Vogiatzis, George},
	year = {2014},
	booktitle = {Proceedings of the British Machine Vision Conference},
	publisher = {BMVA Press},
	editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
	doi = { http://dx.doi.org/10.5244/C.28.123 }
}