In this paper, we consider independence property between a random process and its first derivative. Then, for linear mixtures, we show that cross-correlations between mixtures and...
Sebastien Lagrange, Luc Jaulin, Vincent Vigneron, ...
This paper 1 proposes a technique for simplifying a given Gaussian mixture model, i.e. reformulating the density in a more parcimonious manner, if possible (less Gaussian componen...
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image ...
Recently graph-cut optimization has been extensively explored for interactive image segmentation. In this paper we propose Discriminative Gaussian Mixtures (DGMs) to boost the per...