We study the entropy rate of a hidden Markov process, defined by observing the output of a symmetric channel whose input is a first order Markov process. Although this definition is very simple, obtaining the exact amount of entropy rate in calculation is an open problem. We introduce some probability matrices based on Markov chain's and channel's parameters. Then, we try to obtain an estimate ...

Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task. Regularization on state transitions provides smooth transitioning among states, such that the sequences are split into broad, contiguous segments. Our methods are compared with a recent hierarchical ...

The processing of Japanese text is complicated by the fact that there are no word delimiters. To segment Japanese text, systems typically use knowledge-based methods and large lexicons. This paper presents a novel approach to Japanese word segmentation which avoids the need for Japanese word lexicons and explicit rule bases. The algorithm utilizes a hidden Markov model, a stochastic process, to...

Hidden Markov fields (HMF), which are widely applied in various problems arising in image processing, have recently been generalized to Pairwise Markov Fields (PMF). Although the hidden process is no longer necessarily a Markov one in PMF models, they still allow one to recover it from observed data. We propose in this paper two original methods of parameter estimation in PMF, based on general ...

A general statistical model for the prediction of pronunciation given the orthographic transcript or the canonical pronunciation of a spoken utterance is described. The model is based on a Markov process that can be derived from a set of statistically weighted re-write rules. The automatic learning of such re-write rules based on annotated speech data is illustrated. One possible application of...

OF THE DISSERTATION Graphical Models for Object Segmentation by Rui Huang Dissertation Director: Professor Dimitris N. Metaxas Object segmentation, a fundamental problem in computer vision, remains a challenging task after decades of research efforts. This task is made difficult by the intrinsic variability of the object’s shape, appearance, and its surrounding. It is compounded by the uncertai...

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...

In the classical hidden Markov chain (HMC) model we have a hidden chain X , which is a Markov one and an observed chain Y . HMC are widely used; however, in some situations they have to be replaced by the more general “hidden semi-Markov chains” (HSMC), which are particular “triplet Markov chains” (TMC) ) , , ( Y U X T = , where the auxiliary chain U models the semi-Markovianity of X . Otherwis...