In this paper we apply multi-vector Hidden Markov Random Fields to tissue segmentation of Magnetic Resonance (MR) breast images. Our proposed method performs segmentation using a stack of 3 MR breast slices 1mm apart. The approach takes into account neighborhood voxel information rather than merely neighborhood pixel information and the results are anatomically more plausible in comparison with...

Unsupervised structure learning in high-dimensional time series data has attracted a lot of research interests. For example, segmenting and labelling high dimensional time series can be helpful in behavior understanding and medical diagnosis. Recent advances in generative sequential modeling have suggested to combine recurrent neural networks with state space models (e.g., Hidden Markov Models)...

Hidden Markov models are very important for analysis of signals and systems. In the past two decades they have been attracting the attention of the speech processing community, and recently they have become the favorite models of biologists. Major weakness of conventional hidden Markov models is their inflexibility in modeling state duration. In this paper, we analyze nonstationary hidden Marko...

In this project1, we study the hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm. We implement a MATLAB toolbox named HMRF-EM-image for 2D image segmentation using the HMRF-EM framework2. This toolbox also implements edge-prior-preserving image segmentation, and can be easily reconfigured for other problems, such as 3D image segmentation.

The Infinite Hidden Markov Model (IHMM) extends hidden Markov models to have a countably infinite number of hidden states (Beal et al., 2002; Teh et al., 2006). We present a generalization of this framework that introduces nearly block-diagonal structure in the transitions between the hidden states, where blocks correspond to “subbehaviors” exhibited by data sequences. In identifying such struc...

Multiresolution models such as the wavelet-domain hidden Markov tree (HMT) model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model base...

This thesis presents a prototype system, which integrates statistical and knowledgebased methods, for automatic phonemic segmentation of speech utterances for use in speech production research. First, Aligner, a commercial speech alignment software, synchronizes the speech waveform to the provided text, using hidden Markov models that were trained on phones. Then, a custom built knowledge-based...

Many deoxyribonucleic acid (DNA) sequences display compositional heterogeneity in the form of segments of similar structure. This article describes a Bayesian method that identifies such segments by using a Markov chain governed by a hidden Markov model. Markov chain Monte Carlo (MCMC) techniques are employed to compute all posterior quantities of interest and, in particular, allow inferences t...