A profile hidden Markov model (PHMM) is widely used in assigning protein sequences to protein families. In this model, the hidden states only depend on the previous hidden state and observations are independent given hidden states. In other words, in the PHMM, only the information of the left side of a hidden state is considered. However, it makes sense that considering the information of the b...

This work deals with the unsupervised Bayesian hidden Markov chain restoration extended to the non stationary case. Unsupervised restoration based on “ExpectationMaximization” (EM) or “Stochastic EM” (SEM) estimates considering the “Hidden Markov Chain” (HMC) model is quite efficient when the hidden chain is stationary. However, when the latter is not stationary, the unsupervised restoration re...

Hidden Markov chains (HMC) are a very powerful tool in hidden data restoration and are currently used to solve a wide range of problems. However, when these data are not stationary, estimating the parameters, which are required for unsupervised processing, poses a problem. Moreover, taking into account correlated non-Gaussian noise is difficult without model approximations. The aim of this pape...

Evaluating surgical skill is a time consuming, subjective, and difficult process. This paper compares two methods of identifying the skill level of a subject given motion data from a benchtop surgical task. In the first method, we build discrete Hidden Markov Models at the task level, and test against these models. In the second method, we build discrete Hidden Markov Models of surgical gesture...

We present an approach to model the temporal evolution of audio descriptors using Segmental Models (SMs). This method yields a signal segmentation into a sequence of primitives, constituted by a set of user-defined trajectories . This allows one to consider specific primitive shapes, model their duration and to take into account the time dependence between successive signal frames, contrary to ...

We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. First, an image can be segmented in a way that best matches its statistical model by an approach related to the dynamic programming used for segmenting Markov chains. Second, given an image segmentation, a statistical model (3D state transition matrix and observation distributions withi...

In this paper we present an approach to tree recognition and localisation in orchard environments for tree-crop applications. The method builds on the natural structure of the orchard by first segmenting the data into individual trees using a Hidden Semi-Markov Model. Second, a descriptor for representing the characteristics of the trees is introduced, allowing a Hidden Markov Model based match...