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Journal of Electronic Imaging

Computationally efficient method to compare the shape of planar Gaussian mixtures from point samples
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Paper Abstract

We discuss the problem of recognizing the shape of planar objects consisting of "blobs" that can be modeled as Gaussian mixture densities. We describe an empirical comparison method, assuming a large number of independent samples are given for each distribution. Instead of comparing the Gaussian mixtures directly, we compare the underlying distribution of distances of each mixture. Since distances are invariant under rotations and translations, this provides a workaround to the problem of aligning the objects before comparing them-thus speeding the comparison process. We prove that the distribution of distances is a lossless representation of the shape of generic Gaussian mixtures. Our numerical experiments indicate that, when all the components of the Gaussian mixtures are equally weighted and have the same standard deviation matrix, the proposed method is no less accurate than methods that compare the planar mixtures directly. The extension of our method to the problem of recognizing halftone patterns is briefly discussed.

Paper Details

Date Published: 22 June 2012
PDF: 13 pages
J. Electron. Imaging. 21(2) 023023 doi: 10.1117/1.JEI.21.2.023023
Published in: Journal of Electronic Imaging Volume 21, Issue 2
Show Author Affiliations
Hector J. Santos-Villalobos, Oak Ridge National Lab. (United States)
Mireille Boutin, Purdue Univ. (United States)

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