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Proceedings Paper

Cascading conditional random fields for image registration
Author(s): F. C. Calnegru
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Paper Abstract

This article presents a new Markov Random Field based algorithm for parametric image registration. The algorithm consists in approximating the parameters of the registering transformation, by cascading a number of second order conditional random fields, until a certain condition is met, and then, in refining those parameters through estimating the energy minimum of a third order conditional random field. By casting the registration task in this computational framework, we circumvent the problems associated with estimating the parameters in a higher order Markov Random Field, as well as the accuracy issues introduced by approximating the energy that has to be minimized. The main features of our algorithm are speed, generality, being able to cope with all the types of similarity measures, and accuracy.

Paper Details

Date Published: 24 December 2013
PDF: 10 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670S (24 December 2013); doi: 10.1117/12.2050293
Show Author Affiliations
F. C. Calnegru, Univ. of Pitesti (Romania)


Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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