Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motion-based tracking framework which makes no such assumptions. Object is represented by

How to use the HMM toolbox HMMs with discrete outputs Maximum likelihood parameter estimation using EM (Baum Welch)

Probabilistic Principal Component Analysis – Latent variable models – Probabilistic PCA • Formulation of PCA model • Maximum likelihood estimation – Closed form solution – EM algorithm » EM Algorithms for regular PCA

Using SAS/IML : This code uses the EM algorithm to estimate the maximum likelihood (ML) covariance matrix and mean vector in the presence of missing data. This implementation of the EM algorithm or any similar ML approach assumes that the data are