scholarly journals Multiple Hidden Markov Model for Pathological Vessel Segmentation

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Hu ◽  
Deqiong Ding ◽  
Dianhui Chu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Markov Models ◽  
State Of The Art ◽  
Hidden Markov ◽  
Segmentation Method ◽  
Section Model ◽  
Synthetic Datasets ◽  
Overlap Ratio ◽  
Vessel Axis

One of the obstacles that prevent the accurate delineation of vessel boundaries is the presence of pathologies, which results in obscure boundaries and vessel-like structures. Targeting this limitation, we present a novel segmentation method based on multiple Hidden Markov Models. This method works with a vessel axis + cross-section model, which constrains the classifier around the vessel. The vessel axis constraint gives our method the potential to be both physiologically accurate and computationally effective. Focusing on pathological vessels, we reap the benefits of the redundant information embedded in multiple vessel-specific features and the good statistical properties coming with Hidden Markov Model, to cover the widest possible spectrum of complex situations. The performance of our method is evaluated on synthetic complex-structured datasets, where we achieve a 91% high overlap ratio. We also validate the proposed method on a real challenging case, segmentation of pathological abdominal arteries. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

scholarly journals Utilizing Gaze Behavior for Inferring Task Transitions Using Abstract Hidden Markov Models

2016 ◽  
Vol 19 (58) ◽  
pp. 1
Author(s):  
Daniel Fernando Tello Gamarra
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Hand Movement ◽  
Gaze Behavior ◽  
Improved Method ◽  
The Relationship

We demonstrate an improved method for utilizing observed gaze behavior and show that it is useful in inferring hand movement intent during goal directed tasks. The task dynamics and the relationship between hand and gaze behavior are learned using an Abstract Hidden Markov Model (AHMM). We show that the predicted hand movement transitions occur consistently earlier in AHMM models with gaze than those models that do not include gaze observations.

scholarly journals A Hidden Markov Model for the Linguistic Analysis of the Voynich Manuscript

2019 ◽  
Vol 24 (1) ◽  
pp. 14 ◽  
Author(s):  
Luis Acedo
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Mathematical Linguistics ◽  
Underlying Structure ◽  
Internal States ◽  
Consonant Sounds

Hidden Markov models are a very useful tool in the modeling of time series and any sequence of data. In particular, they have been successfully applied to the field of mathematical linguistics. In this paper, we apply a hidden Markov model to analyze the underlying structure of an ancient and complex manuscript, known as the Voynich manuscript, which remains undeciphered. By assuming a certain number of internal states representations for the symbols of the manuscripts, we train the network by means of the α and β -pass algorithms to optimize the model. By this procedure, we are able to obtain the so-called transition and observation matrices to compare with known languages concerning the frequency of consonant andvowel sounds. From this analysis, we conclude that transitions occur between the two states with similar frequencies to other languages. Moreover, the identification of the vowel and consonant sounds matches some previous tentative bottom-up approaches to decode the manuscript.

Hidden Markov model interpretations of neural networks

2000 ◽  
Vol 23 (4) ◽  
pp. 494-495
Author(s):  
Ingmar Visser
Keyword(s):  
Neural Networks ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Distributed Representations ◽  
Hidden Layer

Page's manifesto makes a case for localist representations in neural networks, one of the advantages being ease of interpretation. However, even localist networks can be hard to interpret, especially when at some hidden layer of the network distributed representations are employed, as is often the case. Hidden Markov models can be used to provide useful interpretable representations.

scholarly journals Five-year trajectories of multimorbidity patterns in an elderly Mediterranean population using Hidden Markov Models

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Concepción Violán ◽  
Sergio Fernández-Bertolín ◽  
Marina Guisado-Clavero ◽  
Quintí Foguet-Boreu ◽  
Jose M. Valderas ◽  
...  
Keyword(s):  
Primary Care ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Markov Models ◽  
Cox Regression ◽  
Hidden Markov ◽  
Population Based ◽  
Specific Pattern ◽  
Mediterranean Population ◽  
Primary Care Population

Abstract This study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%). Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity.

SENTENCE LIPREADING USING HIDDEN MARKOV MODEL WITH INTEGRATED GRAMMAR

2001 ◽  
Vol 15 (01) ◽  
pp. 161-176 ◽  
Author(s):  
KEREN YU ◽  
XIAOYI JIANG ◽  
HORST BUNKE
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Systematic Approach ◽  
Markov Models ◽  
Hidden Markov ◽  
E Mail

In this paper, we describe a systematic approach to the lipreading of whole sentences. A vocabulary of elementary words is considered. Based on the vocabulary, we define a grammar that generates a set of legal sentences. Our lipreading approach is based on a combination of the grammar with hidden Markov models (HMMs). Two different experiments were conducted. In the first experiment a set of e-mail commands is considered, while the set of sentences in the second experiment is given by all English integer numbers up to one million. Both experiments showed promising results, regarding the difficulty of the considered task.

scholarly journals Fuzzy Hidden Markov Models for Indonesian Speech Classification

2012 ◽  
Vol 16 (3) ◽  
pp. 381-387 ◽  
Author(s):  
Intan Nurma Yulita Houw Liong The ◽  
◽  
Adiwijaya ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Speech Signal ◽  
Markov Models ◽  
Hidden Markov ◽  
New Combination ◽  
Speech Classification ◽  
Different Characteristics ◽  
Better Than

Indonesia has many tribes, so that there are many dialects. Speech classification is difficult if the database uses speech signals from various people who have different characteristics because of gender and dialect. The different characteristics will influence frequency, intonation, amplitude, and period of the speech. It makes the system must be trained for the various templates reference of speech signal. Therefore, this study has been developed for Indonesian speech classification. The solution is a new combination of fuzzy on hidden Markov models. The result shows a new version of fuzzy hiddenMarkovmodels is better than hidden Markov model.

scholarly journals An Enhanced Informed Watermarking Scheme Using the Posterior Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Chuntao Wang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
State Of The Art ◽  
Hidden Markov ◽  
Image Watermarking ◽  
Computation Time ◽  
Practical Application ◽  
Robust Watermarking ◽  
Research Topics ◽  
Watermarking Scheme

Designing a practical watermarking scheme with high robustness, feasible imperceptibility, and large capacity remains one of the most important research topics in robust watermarking. This paper presents a posterior hidden Markov model (HMM-) based informed image watermarking scheme, which well enhances the practicability of the prior-HMM-based informed watermarking with favorable robustness, imperceptibility, and capacity. To make the encoder and decoder use the (nearly) identical posterior HMM, each cover image at the encoder and each received image at the decoder are attacked with JPEG compression at an equivalently small quality factor (QF). The attacked images are then employed to estimate HMM parameter sets for both the encoder and decoder, respectively. Numerical simulations show that a small QF of 5 is an optimum setting for practical use. Based on this posterior HMM, we develop an enhanced posterior-HMM-based informed watermarking scheme. Extensive experimental simulations show that the proposed scheme is comparable to its prior counterpart in which the HMM is estimated with the original image, but it avoids the transmission of the prior HMM from the encoder to the decoder. This thus well enhances the practical application of HMM-based informed watermarking systems. Also, it is demonstrated that the proposed scheme has the robustness comparable to the state-of-the-art with significantly reduced computation time.

scholarly journals Map representation using hidden markov models for mobile robot localization

2018 ◽  
Vol 161 ◽  
pp. 03011
Author(s):  
Jesus Savage ◽  
Oscar Fuentes ◽  
Luis Contreras ◽  
Marco Negrete
Keyword(s):  
Mobile Robot ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Robot Localization ◽  
Localization System ◽  
Mobile Robot Localization

This paper describes a map representation and localization system for a mobile robot based on Hidden Markov Models. These models are used not only to find a region where a mobile robot is, but also they find the orientation that it has. It is shown that an estimation of the region where the robot is located can be found using the Viterbi algorithm with quantized laser readings, i.e. symbol observations, of a Hidden Markov Model.

scholarly journals Hidden Markov Model

d'CARTESIAN ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 86 ◽  
Author(s):  
Kezia Tumilaar ◽  
Yohanes Langi ◽  
Altien Rindengan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Learning Problem ◽  
Model Learning ◽  
Observation Sequence ◽  
Evaluation Problem ◽  
Hidden States

Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM)  there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem.  The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the model P(O|) can solved  by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter  to maximize P(O|)  can solved by Baum – Welch algorithm. From description above Hidden Markov Model  with state 3  can describe behavior  from the case studies. Key  words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem

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