Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural inf...

Segmentation is a rst step towards successful tracking and object recognition in 2-D pictures. Mostly the pictures are segmented with respect to quantities as range, intensity etc. Here a method is presented for segmentation of 2-D laser range pictures with respect to both range and variance simultaneously. This is very useful since man-made objects diier from the background in the terrain by t...

We compare a hidden Markov and Erlang semi-Markov modeling of a partially observable deteriorating system operating under a varying load and subject to multi-sensor vibration monitoring. The evolution of the unknown state process is described by a hidden, two state semi-Markov process with an Erlang sojourn time distribution in the healthy state. The unknown model parameters are estimated using...

Classification of musical genres gives a useful measure of similarity and is often the most useful descriptor of a musical piece. Previous techniques to use hidden Markov models (HMMs) for automatic genre classification have used a single HMM to model an entire song or genre. This paper provides a framework to give finer segmentation of HMMs through acoustic segment modeling. Modeling each of t...

This paper deals with the statistical segmentation of multisensor images. In a Bayesian context, the interest of using hidden Markov random fields, which allows one to take contextual information into account, has been well known for about 20 years. In other situations, the Bayesian framework is insufficient and one must make use of the theory of evidence. The aim of our work is to propose evid...

Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses an unsupervised statistical segmentation algorithm to map fire extent, fire front location, just burned area and smoke region based on a statistical model. The results are useful information for a fire propagation model to predict fire behavior. ...