We describe an extension to the Baum-Welch algorithm for training Hidden Markov Models that uses explicit phoneme segmentation to constrain the forward and backward lattice. The HMMs trained with this algorithm can be shown to improve the accuracy of automatic phoneme segmentation. In addition, this algorithm is significantly more computationally efficient than the full BaumWelch algorithm, whi...

Statistical signal modeling using hidden Markov model is one of the techniques used for image compression. Wavelet based statistical signal models are impractical for most of the real time processing because they usually represent the wavelet coefficients as jointly Gaussian or independent to each other. In this paper, we build up an algorithm that succinctly characterizes the interdependencies...

ÐIn this paper, a new analytic scheme, which uses a sequence of segmentation and recognition algorithms, is proposed for offline cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, and stroke width and height are estimated. Second, a segmentation method finds character segmentation paths by combining gray scale and binary information. Third, H...

In this work, we introduce a hidden Markov field model for wavelet image coefficients within a subband and apply it to the image denoising problem. Specifically, we propose to model wavelet image coefficients within subbands as Gaussian random variables with parameters determined by the underlying hidden Markov process. Our model is inspired by the recent Estimation-Quantization (EQ) image code...

We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, ...

A multiscale segmentation strategy using wavelet-domain hidden Markov tree model and pairwise classifiers selection is tested in the present paper for histopathology virtual slide analysis. The classifiers selection is based on a study of the influence of hyper-parameters of the method. Combination of outputs of selected classifiers is then done with majority vote. The results of the segmentati...

A hard problem in time series analysis is often the non-stationarity of the series in the real world. However an important sub-class of nonstationarity is piecewise stationarity, where the series switch between different regimes with finite number of regimes. A motivation to use this model is that each regime can be represented by a state in a finite set and each state match one expert i.e. a m...