Hidden Markov Models: Applications In Computer Vision PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Hidden Markov Models: Applications In Computer Vision PDF full book. Access full book title Hidden Markov Models: Applications In Computer Vision by Horst Bunke. Download full books in PDF and EPUB format.
Author: Horst Bunke
Publisher: World Scientific
ISBN: 9814491470
Category : Computers
Languages : en
Pages : 246
Get Book Here
Book Description
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).
Author: Horst Bunke
Publisher: World Scientific
ISBN: 9814491470
Category : Computers
Languages : en
Pages : 246
Get Book Here
Book Description
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).
Author: Gernot A. Fink
Publisher: Springer Science & Business Media
ISBN: 1447163087
Category : Computers
Languages : en
Pages : 275
Get Book Here
Book Description
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.
Author: Nicu Sebe
Publisher: Springer Science & Business Media
ISBN: 1402032757
Category : Computers
Languages : en
Pages : 253
Get Book Here
Book Description
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
Author: Leszek Rutkowski
Publisher: Springer
ISBN: 364238658X
Category : Computers
Languages : en
Pages : 657
Get Book Here
Book Description
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.
Author: Luis Enrique Sucar
Publisher: Springer Nature
ISBN: 3030619435
Category : Computers
Languages : en
Pages : 370
Get Book Here
Book Description
This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.
Author: Petra Perner
Publisher: Springer Science & Business Media
ISBN: 3540405046
Category : Computers
Languages : en
Pages : 452
Get Book Here
Book Description
TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.
Author: David Barber
Publisher: Cambridge University Press
ISBN: 0521196760
Category : Computers
Languages : en
Pages : 432
Get Book Here
Book Description
The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
Author: Chee Sun Won
Publisher: Springer Science & Business Media
ISBN: 1441988572
Category : Computers
Languages : en
Pages : 176
Get Book Here
Book Description
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.
Author: Michael Lew
Publisher: Springer
ISBN: 3540757732
Category : Computers
Languages : en
Pages : 166
Get Book Here
Book Description
This book constitutes the refereed proceedings of the International Workshop on Human Computer Interaction, HCI 2007. Coverage in the 16 revised full papers presented includes affective detection and recognition, human motion tracking, multimedia data modeling and visualization, HCI issues in image/video retrieval, learning in HCI, input and interaction techniques, perceptual user interfaces, wearable and pervasive technologies in HCI and intelligent virtual environments.
Author: Jayeeta Chattopadhyay
Publisher: Springer
ISBN: 9811331855
Category : Technology & Engineering
Languages : en
Pages : 289
Get Book Here
Book Description
The book presents innovative scientific research works by academics, research scholars and students, presented at the 2017 International Conference on Energy, Materials and Information Technology at Amity University Jharkhand, India. It includes contributions on system solutions based on soft computing techniques, and covers innovative soft computing techniques and tools with advanced applications. A major focus of the book is on presenting interdisciplinary problems and how they can be solved using information technology, together with innovative connections to other disciplines. It also includes papers on cloud computing and WSN-related real-time research.
