Information Theory in Computer Vision and Pattern Recognition

Information Theory in Computer Vision and Pattern Recognition

Author: Francisco Escolano Ruiz

Publisher: Springer Science & Business Media

Published: 2009-07-14

Total Pages: 375

ISBN-13: 1848822979

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Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.


Book Synopsis Information Theory in Computer Vision and Pattern Recognition by : Francisco Escolano Ruiz

Download or read book Information Theory in Computer Vision and Pattern Recognition written by Francisco Escolano Ruiz and published by Springer Science & Business Media. This book was released on 2009-07-14 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.

Information Theory in Computer Vision and Pattern Recognition

Information Theory in Computer Vision and Pattern Recognition

Author: Francisco Escolano Ruiz

Publisher: Springer

Published: 2009-08-29

Total Pages: 364

ISBN-13: 9781848823044

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Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.


Book Synopsis Information Theory in Computer Vision and Pattern Recognition by : Francisco Escolano Ruiz

Download or read book Information Theory in Computer Vision and Pattern Recognition written by Francisco Escolano Ruiz and published by Springer. This book was released on 2009-08-29 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.

Information Theory, Inference and Learning Algorithms

Information Theory, Inference and Learning Algorithms

Author: David J. C. MacKay

Publisher: Cambridge University Press

Published: 2003-09-25

Total Pages: 694

ISBN-13: 9780521642989

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Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.


Book Synopsis Information Theory, Inference and Learning Algorithms by : David J. C. MacKay

Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Information Theory Tools for Image Processing

Information Theory Tools for Image Processing

Author: Miquel Feixas

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 148

ISBN-13: 3031795555

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Information Theory (IT) tools, widely used in many scientific fields such as engineering, physics, genetics, neuroscience, and many others, are also useful transversal tools in image processing. In this book, we present the basic concepts of IT and how they have been used in the image processing areas of registration, segmentation, video processing, and computational aesthetics. Some of the approaches presented, such as the application of mutual information to registration, are the state of the art in the field. All techniques presented in this book have been previously published in peer-reviewed conference proceedings or international journals. We have stressed here their common aspects, and presented them in an unified way, so to make clear to the reader which problems IT tools can help to solve, which specific tools to use, and how to apply them. The IT basics are presented so as to be self-contained in the book. The intended audiences are students and practitioners of image processing and related areas such as computer graphics and visualization. In addition, students and practitioners of IT will be interested in knowing about these applications. Table of Contents: Preface / Acknowledgments / Information Theory Basics / Image Registration / Image Segmentation / Video Key Frame Selection / Informational Aesthetics Measures / Bibliography / Authors' Biographies


Book Synopsis Information Theory Tools for Image Processing by : Miquel Feixas

Download or read book Information Theory Tools for Image Processing written by Miquel Feixas and published by Springer Nature. This book was released on 2022-06-01 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information Theory (IT) tools, widely used in many scientific fields such as engineering, physics, genetics, neuroscience, and many others, are also useful transversal tools in image processing. In this book, we present the basic concepts of IT and how they have been used in the image processing areas of registration, segmentation, video processing, and computational aesthetics. Some of the approaches presented, such as the application of mutual information to registration, are the state of the art in the field. All techniques presented in this book have been previously published in peer-reviewed conference proceedings or international journals. We have stressed here their common aspects, and presented them in an unified way, so to make clear to the reader which problems IT tools can help to solve, which specific tools to use, and how to apply them. The IT basics are presented so as to be self-contained in the book. The intended audiences are students and practitioners of image processing and related areas such as computer graphics and visualization. In addition, students and practitioners of IT will be interested in knowing about these applications. Table of Contents: Preface / Acknowledgments / Information Theory Basics / Image Registration / Image Segmentation / Video Key Frame Selection / Informational Aesthetics Measures / Bibliography / Authors' Biographies

Pattern Recognition, Machine Intelligence and Biometrics

Pattern Recognition, Machine Intelligence and Biometrics

Author: Patrick S. P. Wang

Publisher: Springer Science & Business Media

Published: 2012-02-13

Total Pages: 883

ISBN-13: 3642224075

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"Pattern Recognition, Machine Intelligence and Biometrics" covers the most recent developments in Pattern Recognition and its applications, using artificial intelligence technologies within an increasingly critical field. It covers topics such as: image analysis and fingerprint recognition; facial expressions and emotions; handwriting and signatures; iris recognition; hand-palm gestures; and multimodal based research. The applications span many fields, from engineering, scientific studies and experiments, to biomedical and diagnostic applications, to personal identification and homeland security. In addition, computer modeling and simulations of human behaviors are addressed in this collection of 31 chapters by top-ranked professionals from all over the world in the field of PR/AI/Biometrics. The book is intended for researchers and graduate students in Computer and Information Science, and in Communication and Control Engineering. Dr. Patrick S. P. Wang is a Professor Emeritus at the College of Computer and Information Science, Northeastern University, USA, Zijiang Chair of ECNU, Shanghai, and NSC Visiting Chair Professor of NTUST, Taipei.


Book Synopsis Pattern Recognition, Machine Intelligence and Biometrics by : Patrick S. P. Wang

Download or read book Pattern Recognition, Machine Intelligence and Biometrics written by Patrick S. P. Wang and published by Springer Science & Business Media. This book was released on 2012-02-13 with total page 883 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Pattern Recognition, Machine Intelligence and Biometrics" covers the most recent developments in Pattern Recognition and its applications, using artificial intelligence technologies within an increasingly critical field. It covers topics such as: image analysis and fingerprint recognition; facial expressions and emotions; handwriting and signatures; iris recognition; hand-palm gestures; and multimodal based research. The applications span many fields, from engineering, scientific studies and experiments, to biomedical and diagnostic applications, to personal identification and homeland security. In addition, computer modeling and simulations of human behaviors are addressed in this collection of 31 chapters by top-ranked professionals from all over the world in the field of PR/AI/Biometrics. The book is intended for researchers and graduate students in Computer and Information Science, and in Communication and Control Engineering. Dr. Patrick S. P. Wang is a Professor Emeritus at the College of Computer and Information Science, Northeastern University, USA, Zijiang Chair of ECNU, Shanghai, and NSC Visiting Chair Professor of NTUST, Taipei.

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Author: Ruben Vera-Rodriguez

Publisher: Springer

Published: 2019-03-02

Total Pages: 1001

ISBN-13: 3030134695

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This book constitutes the refereed post-conference proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, held in Madrid, Spain, in November 2018 The 112 papers presented were carefully reviewed and selected from 187 submissions The program was comprised of 6 oral sessions on the following topics: machine learning, computer vision, classification, biometrics and medical applications, and brain signals, and also on: text and character analysis, human interaction, and sentiment analysis


Book Synopsis Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications by : Ruben Vera-Rodriguez

Download or read book Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications written by Ruben Vera-Rodriguez and published by Springer. This book was released on 2019-03-02 with total page 1001 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, held in Madrid, Spain, in November 2018 The 112 papers presented were carefully reviewed and selected from 187 submissions The program was comprised of 6 oral sessions on the following topics: machine learning, computer vision, classification, biometrics and medical applications, and brain signals, and also on: text and character analysis, human interaction, and sentiment analysis

Pattern Recognition and Information Processing

Pattern Recognition and Information Processing

Author: Sergey V. Ablameyko

Publisher: Springer Nature

Published: 2019-11-22

Total Pages: 320

ISBN-13: 303035430X

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This book constitutes the refereed proceedings of the 14th International Conference on Pattern Recognition and Information Processing, PRIP 2019, held in Minsk, Belarus, in May 2019. The 25 revised full papers were carefully reviewed and selected from 120 submissions. The papers of this volume are organized in topical sections on pattern recognition and image analysis; information processing and applications.


Book Synopsis Pattern Recognition and Information Processing by : Sergey V. Ablameyko

Download or read book Pattern Recognition and Information Processing written by Sergey V. Ablameyko and published by Springer Nature. This book was released on 2019-11-22 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th International Conference on Pattern Recognition and Information Processing, PRIP 2019, held in Minsk, Belarus, in May 2019. The 25 revised full papers were carefully reviewed and selected from 120 submissions. The papers of this volume are organized in topical sections on pattern recognition and image analysis; information processing and applications.

Digital Libraries at the Crossroads of Digital Information for the Future

Digital Libraries at the Crossroads of Digital Information for the Future

Author: Adam Jatowt

Publisher: Springer Nature

Published: 2019-10-29

Total Pages: 322

ISBN-13: 3030340589

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This book constitutes the refereed proceedings of the 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, held in Kuala Lumpur, Malaysia, in November 2019. The 13 full, 13 short, and 5 poster papers presented in this volume were carefully reviewed and selected from 54 submissions. The papers were organized in topical sections named: text classification; altmetrics; scholarly data analysis and recommendation; metadata and entities; digital libraries and digital archives management; multimedia processing; search engines; information extraction; and posters.


Book Synopsis Digital Libraries at the Crossroads of Digital Information for the Future by : Adam Jatowt

Download or read book Digital Libraries at the Crossroads of Digital Information for the Future written by Adam Jatowt and published by Springer Nature. This book was released on 2019-10-29 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, held in Kuala Lumpur, Malaysia, in November 2019. The 13 full, 13 short, and 5 poster papers presented in this volume were carefully reviewed and selected from 54 submissions. The papers were organized in topical sections named: text classification; altmetrics; scholarly data analysis and recommendation; metadata and entities; digital libraries and digital archives management; multimedia processing; search engines; information extraction; and posters.

Handbook of Pattern Recognition and Computer Vision

Handbook of Pattern Recognition and Computer Vision

Author: C. H. Chen

Publisher: World Scientific

Published: 1999

Total Pages: 1045

ISBN-13: 9812384731

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The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.


Book Synopsis Handbook of Pattern Recognition and Computer Vision by : C. H. Chen

Download or read book Handbook of Pattern Recognition and Computer Vision written by C. H. Chen and published by World Scientific. This book was released on 1999 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.

Advances in Info-Metrics

Advances in Info-Metrics

Author: Min Chen

Publisher: Oxford University Press

Published: 2020-11-06

Total Pages: 557

ISBN-13: 0190636718

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Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In Advances in Info-Metrics, Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah bring together a group of thirty experts to expand the study of info-metrics across the sciences and demonstrate how to solve problems using this interdisciplinary framework. Building on the theoretical underpinnings of info-metrics, the volume sheds new light on statistical inference, information, and general problem solving. The book explores the basis of information-theoretic inference and its mathematical and philosophical foundations. It emphasizes the interrelationship between information and inference and includes explanations of model building, theory creation, estimation, prediction, and decision making. Each of the nineteen chapters provides the necessary tools for using the info-metrics framework to solve a problem. The collection covers recent developments in the field, as well as many new cross-disciplinary case studies and examples. Designed to be accessible for researchers, graduate students, and practitioners across disciplines, this book provides a clear, hands-on experience for readers interested in solving problems when presented with incomplete and imperfect information.


Book Synopsis Advances in Info-Metrics by : Min Chen

Download or read book Advances in Info-Metrics written by Min Chen and published by Oxford University Press. This book was released on 2020-11-06 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In Advances in Info-Metrics, Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah bring together a group of thirty experts to expand the study of info-metrics across the sciences and demonstrate how to solve problems using this interdisciplinary framework. Building on the theoretical underpinnings of info-metrics, the volume sheds new light on statistical inference, information, and general problem solving. The book explores the basis of information-theoretic inference and its mathematical and philosophical foundations. It emphasizes the interrelationship between information and inference and includes explanations of model building, theory creation, estimation, prediction, and decision making. Each of the nineteen chapters provides the necessary tools for using the info-metrics framework to solve a problem. The collection covers recent developments in the field, as well as many new cross-disciplinary case studies and examples. Designed to be accessible for researchers, graduate students, and practitioners across disciplines, this book provides a clear, hands-on experience for readers interested in solving problems when presented with incomplete and imperfect information.