NDLI: An exploration of multimodal similarity metrics for parametric image registration based on particle filtering

Content Provider	IEEE Xplore Digital Library
Author	Reducindo, I. Arce-Santana, E.R. Campos-Delgado, D.U. Vigueras-Gomez, J.F. Alba, A.
Copyright Year	2011
Description	Author affiliation: Universidad Aut ónoma de San Luis Potosí, Facultad de Ciencias, Diagonal Sur S/N, Zona Universitaria, C.P. 78290, San Luis Potosí, México (Reducindo, I.; Arce-Santana, E.R.; Campos-Delgado, D.U.; Vigueras-Gomez, J.F.; Alba, A.)
Abstract	This paper presents an analysis of different multimodal similarity metrics for parametric image registration based on particle filtering. Our analysis includes four similarity metrics found in the literature and we propose a new metric based on the discretization of the kernel predictability, function recently introduced by Gómez-García et al. (2008), that we call histogram kernel predictability (HKP). Hence the metrics studied in this work are mutual information, normalized mutual information, kernel predictibility with gaussian and truncated parabola functions, and HKP. The evaluations include tests varying the number of particles in the filter, the type of pixel sampling, the number of bins used to calculate the histograms, the noise in the images, and the computation time. Furthermore, we also conducted a geometric analysis to inspect convexity properties of the metrics under discussion. The overall evaluation suggests that the normalized mutual information is the best similarity metric for parametric image registration.
Starting Page	1
Ending Page	6
File Size	601442
Page Count	6
File Format	PDF
ISBN	9781457710117
e-ISBN	9781457710131
DOI	10.1109/ICEEE.2011.6106635
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-26
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image registration Histograms Image matching Noise Particle measurements Particle filters Kernel Mutual information
Content Type	Text
Resource Type	Article

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