A method for labelling edge data in registered intensity and range images is presented. First, depth, orientation and intensity discontinuities are identified by one-dimensional filters aligned with the image axes. Second, the filtered outputs are combined by a maximum a-posteriori probability estimation to produce a set of edge labels at each site within the dual lattice.

In this contribution three examples of techniques that can be used for state order estimation of hidden Markov models are given. The methods are also exem-pliied using real laser range data, and the computational burden of the three methods is discussed. Two techniques, Maximum Description Length and Maximum a Posteriori Estimate, are shown to be very similar under certain circumstances. The th...

Model-based clustering for functional data is considered. An alternative to model-based clustering using the functional principal components is proposed by approximating the density of functional random variables. An EM-like algorithm is used for parameter estimation and the maximum a posteriori rule provides the clusters. Real data applications illustrate the interest of the proposed methodology.

We present a probabilistic switching state space model for timing deviations in expressive music performance. We formulate tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. The resulting model is suitable for real-time tempo tracking and transcription and hence useful in a number of music applications such as ada...

Abstract The Bayesian solution to a statistical inverse problem can be summarised by mode of the posterior distribution, i.e. maximum posteriori (MAP) estimator. MAP estimator essentially coincides with (regularised) variational problem, seen as minimisation Onsager–Machlup (OM) functional measure. An open in stability analysis problems is establish relationship between convergence properties s...

In this paper a framework for maximum a posteriori (MAP) estimation of hidden Markov models (HMM) is presented. Three key issues of MAP estimation, namely the choice of prior distribution family, the specification of the parameters of prior densities and the evaluation of the MAP estimates, are addressed. Using HMMs with Gaussian mixture state observation densities as an example, it is assumed ...

The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectr...

Thick-slice image acquisitions are sometimes inevitable in magnetic resonance imaging due to limitations posed by pulse sequence timing and signal-to-noise-ratio. The estimation of an isotropic high-resolution volume from thick-slice MRI scans is desired for improved image analysis and evaluation. In this article we formulate a maximum a posteriori (MAP) estimation algorithm for high-resolution...