Joint Multitarget Detection and Tracking in Multipath Environment Using Expectation Maximization Algorithm

Fluorescence lifetime discrimination using expectation-maximization algorithm with joint deconvolution.

The fluorescence lifetime technique offers an effective way to resolve fluorescent components with overlapping emission spectra. The presence of multiple fluorescent components in biological compounds can hamper their discrimination. The conventional method based on the nonlinear least-squares technique is unable to consistently determine the correct number of fluorescent components in a fluore...

Expectation Maximization Deconvolution Algorithm

In this paper, we use a general mathematical and experimental methodology to analyze image deconvolution. The main procedure is to use an example image convolving it with a know Gaussian point spread function and then develop algorithms to recover the image. Observe the deconvolution process by adding Gaussian and Poisson noise at different signal to noise ratios. In addition, we will describe ...

The Expectation Maximization Algorithm

This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slight variation on TomMinka’s tutorial (Minka, 1998), perhaps a little easier (or perhaps not). It includes a graphical example to provide some intuition. 1 Intuitive Explanation of EM EM is an iterative optimizationmethod to estimate some unknown ...

Expectation-Maximization (EM) algorithm

MOTEXATION: Multi-Object Tracking with the Expectation-Maximization Algorithm

The paper proposes a new edge-based multi-object tracking framework, MOTEXATION, which deals with tracking multiple objects with occlusions using the Expectation-Maximization (EM) algorithm and a novel edge-based appearance model. In the edge-based appearance model, an object is modelled by a mixture of a non-parametric contour model and a non-parametric edge model using kernel density estimati...