A method for recognizing the shape of a Gaussian mixture from a sparse sample set

Hector J. Santos-Villalobos; Mireille Boutin

doi:10.1117/12.848604

27 January 2010 A method for recognizing the shape of a Gaussian mixture from a sparse sample set

Hector J. Santos-Villalobos, Mireille Boutin

Proceedings Volume 7533, Computational Imaging VIII; 753305 (2010) https://doi.org/10.1117/12.848604
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

The motivating application for this research is the problem of recognizing a planar object consisting of points from a noisy observation of that object. Given is a planar Gaussian mixture model _ρ_T (x) representing an object along with a noise model for the observation process (the template). Also given are points representing the observation of the object (the query). We propose a method to determine if these points were drawn from a Gaussian mixture _ρ_Q(x) with the same shape as the template. The method consists in comparing samples from the distribution of distances of _ρ_T (x) and _ρ_Q(x), respectively. The distribution of distances is a faithful representation of the shape of generic Gaussian mixtures. Since it is invariant under rotations and translations of the Gaussian mixture, it provides a workaround to the problem of aligning objects before recognizing their shape without sacrificing accuracy. Experiments using synthetic data show a robust performance against type I errors, and few type II errors when the given template Gaussian mixtures are well distinguished.

Citation Download Citation

Hector J. Santos-Villalobos and Mireille Boutin "A method for recognizing the shape of a Gaussian mixture from a sparse sample set", Proc. SPIE 7533, Computational Imaging VIII, 753305 (27 January 2010); https://doi.org/10.1117/12.848604

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