A texture segmentation algorithm is developed, utilizing a wavelet-based multi-resolution analysis of general imagery. The wavelet analysis yields a set of quadtrees, each composed of highhigh (HH), high-low (HL) and low-high (LH) wavelet coefficients. Hidden Markov trees (HMTs) are designed for the quadtree HH, HL and LH wavelet coefficients. Many textures have intricate structure, extending b...

We discuss a framework for modeling the switching dynamics of a time series based on hidden Markov models (HMM) of prediction experts, here neural networks. Learning is treated as a maximum likelihood problem. In particular, we present an Expectation-Maximization (EM) algorithm for adjusting the expert parameters as well as the HMM transition probabilities. Based on this algorithm, we develop a...

Hidden Markov modeling has proven extremely useful for statistical analysis of speech signals. There are, however, inherent problems in two dimensional extensions to HMM's, one of which is the exponential complexity associated with fully 2-D HMM's. In this paper, we propose a new 2-D HMM-like structure obtained by embedding states within regions of a deformable template structure. With this sta...

Recognition of Emotion can be identified using Eye Tracking methods which may be non-intrusive. SVD and HMM are used for eye tracking to recognize emotions, which classifies six different emotions with less correlation coefficiency and 77% accuracy is achieved. This work also focus on emotion recognition with HMM using the distance calculation method ,measuring sclera and iris distance.A fully ...

Continuing progress in the automatic transcription of broadcast speech via speech recognition has raised the possibility of applying information retrieval techniques to the resulting (errorful) text. In this paper we describe a general methodology based on Hidden Markov Models and classical language modeling techniques for automatically inferring story boundaries (segmentation) and for retrievi...

This work deals with the statistical restoration of a hidden signal using Pairwise Markov Trees (PMT). PMT have been introduced recently in the case of a discrete hidden signal. We first show that PMT can perform better than the classical Hidden Markov Trees (HMT) when applied to unsupervised image segmentation. We next consider a PMT in a linear Gaussian model with continuous hidden data, and ...