Contour Level Estimation from Gaussian Mixture Models Applied to Nonlinear Bss
نویسندگان
چکیده
Probability density function estimation from limited data sets is a classical problem in pattern recognition. In this paper we propose a reformulation of the well-known nonparametric Parzen method as a parametrically regularized Gaussian Mixture Model, from which we can easily estimate density contour level. As an application illustration to the proposed contour level estimator, we also address the Blind Source Separation problem through the analysis of contour level distortions in joint probability density functions. Finally, we use the proposed estimator to undo a nonlinear mixture of two images.
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