This paper describes a Bayesian approach to determining the number of hidden states in a hidden Markov model (HMM) via reversible jump Markov chain Monte Carlo (MCMC) methods. Acceptance rates for these algorithms can be quite low, resulting in slow exploration of the posterior distribution. We consider a variety of reversible jump strategies which allow inferences to be made in discretely obse...

The problem of modeling chaotic nonlinear dynamical systems using hidden Markov models is considered. A hidden Markov model for a class of chaotic systems is developed from noise-free observations of the output of that system. A combination of vector quantization and the Baum-Welch algorithm is used for training. The importance of this combined iterative approach is demonstrated. The model is t...

This paper is concerned with the segmentation of images from an image sequence representing a dynamic scene. We assume a rapid scene sampling producing a situation similar to the short range motion process of the human visual system. Image segmentation is the division of the image into different regions, each having certain properties. Here, as in a number of our previous works with static imag...

ABSTRACT Many image processing problems can be presented as inverse problems by modeling the relation of the observed image to the unknown desired features explicitly. Some of these problems are naturally presented as inverse problems such as restoration of blurred images (deconvolution) or image reconstruction in computed tomography. For some others, we need to translate the original problem a...

Keywor ds : speech segmentation, speech recognition, recurrent neural networks, extended recurrent neural networks, bi-directional recurrent neural networks, hidden Markov models Speech recognition is fast becoming an attractive form of communicat ion between humans and machines, due to the recent advances made in the field. The technology is, however, far from being acceptable when used in an ...

This paper presents a fast face recognition (FR) method using only three states of Hidden Markov Model (HMM), where the number of states is a major effective factor in computational complexity. Most of the researchers believe that each state represents one facial region, so they used five states or more according to the number of facial regions. In this work, a different idea has been proven, w...

We investigate the automatic phonetic modeling of sign language based on phonetic sub-units, which are data driven and without any prior phonetic information. Visual processing is based on a probabilistic skin color model and a framewise geodesic active contour segmentation; occlusions are handled by a forward-backward prediction component leading finally to simple and effective region-based vi...

Affect and cognition intertwine throughout human experience. Research into this interplay during learning has identified relevant cognitiveaffective states, but recognizing them poses significant challenges. Among multiple promising approaches for affect recognition, analyzing facial expression may be particularly informative. Descriptive computational models of facial expression and affect, su...

In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs). We first review recent supervised Bayesian image segmentation algorithms using wavelet-domain HMMs. Then, a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs. The K-mean clustering is u...