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. © 2011 IEEE.

authors

• Alba Cadena Francisco Alfonso
• Arce Santana Edgar Román
• Campos Delgado Daniel Ulises
• Reducindo Ruiz Isnardo
• Vigueras Gómez Flavio

publication date

• 2011-01-01

published in

• CCE 2011 - 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, Program and Abstract Book  Proceedings

keywords

• image matching; Image registration; mutual information; particle filters Computation time; Convexity properties; Discretizations; Gaussians; Geometric analysis; Multi-modal; Mutual informations; Normalized mutual information; Particle Filtering; particle filters; Predictibility; Similarity metrics; Automation; Control; Electrical engineering; Function evaluation; Graphic methods; Image matching; Image registration; Process control; Statistical methods; Signal filtering and prediction

Digital Object Identifier (DOI)

• 10.1109/ICEEE.2011.6106635

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