Hidden Markov Models: Applications In Computer Vision

Hidden Markov Models: Applications In Computer Vision

Author: Horst Bunke

Publisher: World Scientific

Published: 2001-06-04

Total Pages: 246

ISBN-13: 9814491470

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Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001).


Book Synopsis Hidden Markov Models: Applications In Computer Vision by : Horst Bunke

Download or read book Hidden Markov Models: Applications In Computer Vision written by Horst Bunke and published by World Scientific. This book was released on 2001-06-04 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001).

Hidden Markov Models for Pattern Recognition and Computer Vision

Hidden Markov Models for Pattern Recognition and Computer Vision

Author: Manuele Bicego

Publisher:

Published: 2002

Total Pages: 205

ISBN-13:

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Book Synopsis Hidden Markov Models for Pattern Recognition and Computer Vision by : Manuele Bicego

Download or read book Hidden Markov Models for Pattern Recognition and Computer Vision written by Manuele Bicego and published by . This book was released on 2002 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Markov Models for Pattern Recognition

Markov Models for Pattern Recognition

Author: Gernot A. Fink

Publisher: Springer Science & Business Media

Published: 2014-01-14

Total Pages: 275

ISBN-13: 1447163087

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This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.


Book Synopsis Markov Models for Pattern Recognition by : Gernot A. Fink

Download or read book Markov Models for Pattern Recognition written by Gernot A. Fink and published by Springer Science & Business Media. This book was released on 2014-01-14 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.

Stochastic Image Processing

Stochastic Image Processing

Author: Chee Sun Won

Publisher: Springer Science & Business Media

Published: 2013-11-27

Total Pages: 176

ISBN-13: 1441988572

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Stochastic Image Processing provides the first thorough treatment of Markov and hidden Markov random fields and their application to image processing. Although promoted as a promising approach for over thirty years, it has only been in the past few years that the theory and algorithms have developed to the point of providing useful solutions to old and new problems in image processing. Markov random fields are a multidimensional extension of Markov chains, but the generalization is complicated by the lack of a natural ordering of pixels in multidimensional spaces. Hidden Markov fields are a natural generalization of the hidden Markov models that have proved essential to the development of modern speech recognition, but again the multidimensional nature of the signals makes them inherently more complicated to handle. This added complexity contributed to the long time required for the development of successful methods and applications. This book collects together a variety of successful approaches to a complete and useful characterization of multidimensional Markov and hidden Markov models along with applications to image analysis. The book provides a survey and comparative development of an exciting and rapidly evolving field of multidimensional Markov and hidden Markov random fields with extensive references to the literature.


Book Synopsis Stochastic Image Processing by : Chee Sun Won

Download or read book Stochastic Image Processing written by Chee Sun Won and published by Springer Science & Business Media. This book was released on 2013-11-27 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Image Processing provides the first thorough treatment of Markov and hidden Markov random fields and their application to image processing. Although promoted as a promising approach for over thirty years, it has only been in the past few years that the theory and algorithms have developed to the point of providing useful solutions to old and new problems in image processing. Markov random fields are a multidimensional extension of Markov chains, but the generalization is complicated by the lack of a natural ordering of pixels in multidimensional spaces. Hidden Markov fields are a natural generalization of the hidden Markov models that have proved essential to the development of modern speech recognition, but again the multidimensional nature of the signals makes them inherently more complicated to handle. This added complexity contributed to the long time required for the development of successful methods and applications. This book collects together a variety of successful approaches to a complete and useful characterization of multidimensional Markov and hidden Markov models along with applications to image analysis. The book provides a survey and comparative development of an exciting and rapidly evolving field of multidimensional Markov and hidden Markov random fields with extensive references to the literature.

Artificial Intelligence and Soft Computing

Artificial Intelligence and Soft Computing

Author: Leszek Rutkowski

Publisher: Springer

Published: 2013-06-04

Total Pages: 657

ISBN-13: 364238658X

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The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised full papers presented together with one invited paper were carefully reviewed and selected from 274 submissions. The 57 papers included in the first volume are organized in the following topical sections: neural networks and their applications; fuzzy systems and their applications; pattern classification; and computer vision, image and speech analysis.


Book Synopsis Artificial Intelligence and Soft Computing by : Leszek Rutkowski

Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer. This book was released on 2013-06-04 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised full papers presented together with one invited paper were carefully reviewed and selected from 274 submissions. The 57 papers included in the first volume are organized in the following topical sections: neural networks and their applications; fuzzy systems and their applications; pattern classification; and computer vision, image and speech analysis.

Hidden Markov Models and Applications

Hidden Markov Models and Applications

Author: Nizar Bouguila

Publisher: Springer Nature

Published: 2022-05-19

Total Pages: 303

ISBN-13: 3030991423

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This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.


Book Synopsis Hidden Markov Models and Applications by : Nizar Bouguila

Download or read book Hidden Markov Models and Applications written by Nizar Bouguila and published by Springer Nature. This book was released on 2022-05-19 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.

Hidden Markov Models

Hidden Markov Models

Author: Przemyslaw Dymarski

Publisher: BoD – Books on Demand

Published: 2011-04-19

Total Pages: 329

ISBN-13: 9533072083

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Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.


Book Synopsis Hidden Markov Models by : Przemyslaw Dymarski

Download or read book Hidden Markov Models written by Przemyslaw Dymarski and published by BoD – Books on Demand. This book was released on 2011-04-19 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.

Hidden Semi-Markov Models

Hidden Semi-Markov Models

Author: Shun-Zheng Yu

Publisher: Morgan Kaufmann

Published: 2015-10-22

Total Pages: 209

ISBN-13: 0128027711

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Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science. Discusses the latest developments and emerging topics in the field of HSMMs Includes a description of applications in various areas including, Human Activity Recognition, Handwriting Recognition, Network Traffic Characterization and Anomaly Detection, and Functional MRI Brain Mapping. Shows how to master the basic techniques needed for using HSMMs and how to apply them.


Book Synopsis Hidden Semi-Markov Models by : Shun-Zheng Yu

Download or read book Hidden Semi-Markov Models written by Shun-Zheng Yu and published by Morgan Kaufmann. This book was released on 2015-10-22 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science. Discusses the latest developments and emerging topics in the field of HSMMs Includes a description of applications in various areas including, Human Activity Recognition, Handwriting Recognition, Network Traffic Characterization and Anomaly Detection, and Functional MRI Brain Mapping. Shows how to master the basic techniques needed for using HSMMs and how to apply them.

Markov Models

Markov Models

Author: Robert Wilson

Publisher: Createspace Independent Publishing Platform

Published: 2017-06-10

Total Pages: 150

ISBN-13: 9781548002206

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Do you want to become a data science Savvy? If reading about Markov models, stochastic processes, and probabilities leaves you scratching your head, then you have definitely come to the right place. If you are looking for the most no-nonsense guide that will keep you on the right course during the turbulent ride filled with scientific enigmas, machine learning, and predicting probabilities of hidden, unobservable states, then you have found your perfect companion. This book will Cover: What is Markov models How to make predictions with Markov Models How to learn without supervision How do Markov Models use prediction? Hidden Markov Models and how to use them The secrets of Markov Chains Tips and tricks on how to use Markov Models and machine learning Markov Models with Python Markov Models Examples and predictions How to build and implement HMM algorithms How to use Markov Models to master machine learning The secrets of Supervised and unsupervised machine learning The three components of Hidden Markov Models And much, much more! By the end of this book, I guarantee that you will dive easily into the data science world. Save yourself the hard work and frustration by downloading this book today. Download your free copy today (Kindle Unlimited only)


Book Synopsis Markov Models by : Robert Wilson

Download or read book Markov Models written by Robert Wilson and published by Createspace Independent Publishing Platform. This book was released on 2017-06-10 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to become a data science Savvy? If reading about Markov models, stochastic processes, and probabilities leaves you scratching your head, then you have definitely come to the right place. If you are looking for the most no-nonsense guide that will keep you on the right course during the turbulent ride filled with scientific enigmas, machine learning, and predicting probabilities of hidden, unobservable states, then you have found your perfect companion. This book will Cover: What is Markov models How to make predictions with Markov Models How to learn without supervision How do Markov Models use prediction? Hidden Markov Models and how to use them The secrets of Markov Chains Tips and tricks on how to use Markov Models and machine learning Markov Models with Python Markov Models Examples and predictions How to build and implement HMM algorithms How to use Markov Models to master machine learning The secrets of Supervised and unsupervised machine learning The three components of Hidden Markov Models And much, much more! By the end of this book, I guarantee that you will dive easily into the data science world. Save yourself the hard work and frustration by downloading this book today. Download your free copy today (Kindle Unlimited only)

Robust Computer Vision

Robust Computer Vision

Author: N. Sebe

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 226

ISBN-13: 9401702950

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From the foreword by Thomas Huang: "During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood framework, Bayesian Networks, and Hidden Markov models are also used. Three aspects are stressed: features, similarity metric, and models. Many interesting and important new results, based on research by the authors and their collaborators, are presented. Although this book contains many new results, it is written in a style that suits both experts and novices in computer vision."


Book Synopsis Robust Computer Vision by : N. Sebe

Download or read book Robust Computer Vision written by N. Sebe and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the foreword by Thomas Huang: "During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood framework, Bayesian Networks, and Hidden Markov models are also used. Three aspects are stressed: features, similarity metric, and models. Many interesting and important new results, based on research by the authors and their collaborators, are presented. Although this book contains many new results, it is written in a style that suits both experts and novices in computer vision."