Studies in Computational Intelligence 386 Marek r. ogiela Lakhmi c Jain(Eds Computational Intelligence Paradigms in Advanced pattern Classification S pringer
Studies in Computational Intelligence, Volume 386 Editor-in-Chief Prof Janusz Kacprzyk olish Academy of Sciences ul. Newelska 6 01-447 Warsaw E-mail: kacprzyk@ibspan waw pl Further volumes of this series can be found on our aglen Biba and Fatos Xhafa(Eds) VoL. 363. Kishan G Mehrotra, Chilukuri Mohan, Jae C. Oh, ISBN978-3- Pramod K Varshney, and Moonis Ali(Eds. Applied Intelligence, 2011 be and Lakhmi C Jain(Eds ISBN978-3-642-21331-1 Machines-2 2011 ISBN 978-3- VoL. 364. Roger Lee(Ec VoL. 377. Roger Lee(Ed) sBN978-3-642-21377-9 search, Management ISBN978-3-642-23201 stems, and Industrial VoL. 378. Janos Fodor, Ryszard Klempous, an Engineering 20 BN978-3-642-21374-8 igent Engineering systems, 2011 Mario Koppen, Gerald Schaefer, and ISBN978-3-642-23228-2 nal Optimization in Engineering 2011 Vol. 379. Ferrante Neri, Carlos Cotta, and andbook of Memetic algorithms, 2011 Gabriel Luque and Enrique Alba ISBN978-3-642-23246-6 Genetic algorithms, 2011 ISBN978-3-642- VoL. 380. Anthony Brabazon, Michael O'Neill, and (Ed) Natural Computing in Computational Finance, 2011 Software Engineering, Artific Networking and ISBN 978-3-642-23335-7 ISBN978-3-642-22287-0 vol.381. Chao-Fu Hong, and Ngoc Thanh Nguyen(Eds. J VoL. 369. Dominik Ryzko, Piotr Gawrysiak, Henryk Rybinski, Semantic Methods for Knowledge Management and dustry, 2011 ISBN978-3-642-23417-0 978-3-642-22731-8 VoL. 382. E.M. T Brazier, Kees Nieuwenhuis, Gregor Pavlin Mehler, Kai-Uwe Kohnberger, Martijn Warnier, and Costin Badica(Eds. Henning Lobin, Harald Langen, Angelika Storrer, ISBN978-3-642-24012-6 VoL. 383. Takayuki Shaheen Fatima, Iro Matsuo(Eds VoL. 371. Leonid Perlovsky, Ross Deming, and ISBN978-3-642-24695-1 onal Cognitive Neural algorithms with Engineering Applications, 2011 shall, J&rn Anemaller ISBN978-3-642-22829-2 ation of Rare Audiovisual Cues, 201 Annamaria R. Varkonyi-K6czy(Eds New Advances in Intelligent Signal Processing, 2011 leg Okun, Giorgio Valentini, and Matteo Re(Eds Networks 20, ence Labelling with Recurrent Neural BN978-3-642-11738-1 I Machine Learning Applicat SBN978-3-642-22909-1 VoL. 374. Dimitri Plemenos and Georgios Miaoulis(Eds)
Studies in Computational Intelligence,Volume 386 Editor-in-Chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw Poland E-mail: kacprzyk@ibspan.waw.pl Further volumes of this series can be found on our homepage: springer.com Vol. 363. Kishan G. Mehrotra, Chilukuri Mohan, Jae C. Oh, Pramod K.Varshney, and Moonis Ali (Eds.) Developing Concepts in Applied Intelligence, 2011 ISBN 978-3-642-21331-1 Vol. 364. Roger Lee (Ed.) Computer and Information Science, 2011 ISBN 978-3-642-21377-9 Vol. 365. Roger Lee (Ed.) Computers, Networks, Systems, and Industrial Engineering 2011, 2011 ISBN 978-3-642-21374-8 Vol. 366. Mario Köppen, Gerald Schaefer, and Ajith Abraham (Eds.) Intelligent Computational Optimization in Engineering, 2011 ISBN 978-3-642-21704-3 Vol. 367. Gabriel Luque and Enrique Alba Parallel Genetic Algorithms, 2011 ISBN 978-3-642-22083-8 Vol. 368. Roger Lee (Ed.) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011, 2011 ISBN 978-3-642-22287-0 Vol. 369. Dominik Ry_zko, Piotr Gawrysiak, Henryk Rybinski, and Marzena Kryszkiewicz (Eds.) Emerging Intelligent Technologies in Industry, 2011 ISBN 978-3-642-22731-8 Vol. 370.Alexander Mehler, Kai-Uwe Kühnberger, Henning Lobin, Harald Lüngen,Angelika Storrer, and Andreas Witt (Eds.) Modeling, Learning, and Processing of Text Technological Data Structures, 2011 ISBN 978-3-642-22612-0 Vol. 371. Leonid Perlovsky, Ross Deming, and Roman Ilin (Eds.) Emotional Cognitive Neural Algorithms with Engineering Applications, 2011 ISBN 978-3-642-22829-2 Vol. 372.Ant´onio E. Ruano and Annam´aria R.V´arkonyi-K´oczy (Eds.) New Advances in Intelligent Signal Processing, 2011 ISBN 978-3-642-11738-1 Vol. 373. Oleg Okun, Giorgio Valentini, and Matteo Re (Eds.) Ensembles in Machine Learning Applications, 2011 ISBN 978-3-642-22909-1 Vol. 374. Dimitri Plemenos and Georgios Miaoulis (Eds.) Intelligent Computer Graphics 2011, 2011 ISBN 978-3-642-22906-0 Vol. 375. Marenglen Biba and Fatos Xhafa (Eds.) Learning Structure and Schemas from Documents, 2011 ISBN 978-3-642-22912-1 Vol. 376. Toyohide Watanabe and Lakhmi C. Jain (Eds.) Innovations in Intelligent Machines – 2, 2011 ISBN 978-3-642-23189-6 Vol. 377. Roger Lee (Ed.) Software Engineering Research, Management and Applications 2011, 2011 ISBN 978-3-642-23201-5 Vol. 378. János Fodor, Ryszard Klempous, and Carmen Paz Suárez Araujo (Eds.) Recent Advances in Intelligent Engineering Systems, 2011 ISBN 978-3-642-23228-2 Vol. 379. Ferrante Neri, Carlos Cotta, and Pablo Moscato (Eds.) Handbook of Memetic Algorithms, 2011 ISBN 978-3-642-23246-6 Vol. 380.Anthony Brabazon, Michael O’Neill, and Dietmar Maringer (Eds.) Natural Computing in Computational Finance, 2011 ISBN 978-3-642-23335-7 Vol. 381. Radoslaw Katarzyniak, Tzu-Fu Chiu, Chao-Fu Hong, and Ngoc Thanh Nguyen (Eds.) Semantic Methods for Knowledge Management and Communication, 2011 ISBN 978-3-642-23417-0 Vol. 382. F.M.T. Brazier, Kees Nieuwenhuis, Gregor Pavlin, Martijn Warnier, and Costin Badica (Eds.) Intelligent Distributed Computing V, 2011 ISBN 978-3-642-24012-6 Vol. 383. Takayuki Ito, Minjie Zhang,Valentin Robu, Shaheen Fatima, and Tokuro Matsuo (Eds.) New Trends in Agent-based Complex Automated Negotiations, 2011 ISBN 978-3-642-24695-1 Vol. 384. Daphna Weinshall, J¨orn Anem¨uller, and Luc van Gool (Eds.) Detection and Identification of Rare Audiovisual Cues, 2011 ISBN 978-3-642-24033-1 Vol. 385.Alex Graves Supervised Sequence Labelling with Recurrent Neural Networks, 2012 ISBN 978-3-642-24796-5 Vol. 386. Marek R. Ogiela and Lakhmi C. Jain (Eds.) Computational Intelligence Paradigms in Advanced Pattern Classification, 2012 ISBN 978-3-642-24048-5
Editors Prof. Marek R. Ogiela Prof Lakhmi C Jain H University of Science and Techi University of South Australia 30 Mickiewicz Ave Adelaide Mawson Lakes Campus South australia E-mail: mogiela@agh. edu. pl australia ISBN978-3-642-24048-5 e-ISBN978-3-642-24049-2 DOI10.1007/978-3-64224049-2 Studies in Computational Intelligence ISSN1860-949X Library of Congress Control Number: 2011936648 c 2012 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part f the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefo free for general use. Typeset & Cover Design: Scientific Publishing Services Pvt. Ltd, Chennai, India. Printed on acid-fre 987654321 springer. com
Editors Prof. Marek R. Ogiela AGH University of Science and Technology 30 Mickiewicza Ave 30-059 Krakow Poland E-mail: mogiela@agh.edu.pl Prof. Lakhmi C. Jain University of South Australia Adelaide Mawson Lakes Campus South Australia Australia E-mail: Lakhmi.jain@unisa.edu.au ISBN 978-3-642-24048-5 e-ISBN 978-3-642-24049-2 DOI 10.1007/978-3-642-24049-2 Studies in Computational Intelligence ISSN 1860-949X Library of Congress Control Number: 2011936648 c 2012 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typeset & Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India. Printed on acid-free paper 987654321 springer.com
Preface Recent advances in intelligent computational intelligence paradigms have contributed tremendously in modem pattern classification techniques. This book is aimed to pro- vide a sample of the state of art techniques in advanced pattern classification and its possible applications. In particular this book includes nine chapters on using various computational o telligent paradigms in healthcare such as intelligent agents and case-based rea- soning. Additionally a number of applications and case studies are presente Chapter one presents an introduction to pattern classification techniques includ- ng current trends in intelligent image analysis and semantic content description Chapter two is on handwriting recognition using neural networks. The authors have proposed a novel neural network. It is demonstrated that the proposed technique of fers higher recognition rate than the other reported techniques in the literature. Chapter three is on moving object detection from mobile platforms. The authors have demonstrated the applicability of their approach to detect moving objects like vehicles or pedestrian in different urban scenarios. Chapter four is on pattern classifications in cognitive environments. The author has demonstrated experimentally that the semantic technique can be used for cog nitive data analysis problems in cognitive informatics. Chapter five is on optimal differential filters on hexagonal lattice. The filters are compared with existing optimised filters to demonstrate the superiority of the technique Chapter six is on graph image language techniques supporting advanced classi fication and computer interpretation of 3D CT coronary vessel visualizations Chapter seven is on a graph matching approach to symmetry detection and analy sis. The authors have validated their approach using extensive experiments on two and three dimensional synthetic and real life images Chapter eight is on pattern classification methods used for the analysis of brain visualization and Computer-aided Diagnosis of perfusion CT maps. The final chapter is on the main methods of multi-class and multi-label classification. These can be applied to a large variety of applications and research fields that relate to human knowledge, cognition and behaviour We believe that scientists, application engineers, university professors, students, and all interested with this subject readers will find this book useful and interesting
Preface Recent advances in intelligent computational intelligence paradigms have contributed tremendously in modern pattern classification techniques. This book is aimed to provide a sample of the state of art techniques in advanced pattern classification and its possible applications. In particular this book includes nine chapters on using various computational intelligent paradigms in healthcare such as intelligent agents and case-based reasoning. Additionally a number of applications and case studies are presented. Chapter one presents an introduction to pattern classification techniques including current trends in intelligent image analysis and semantic content description. Chapter two is on handwriting recognition using neural networks. The authors have proposed a novel neural network. It is demonstrated that the proposed technique offers higher recognition rate than the other reported techniques in the literature. Chapter three is on moving object detection from mobile platforms. The authors have demonstrated the applicability of their approach to detect moving objects like vehicles or pedestrian in different urban scenarios. Chapter four is on pattern classifications in cognitive environments. The author has demonstrated experimentally that the semantic technique can be used for cognitive data analysis problems in cognitive informatics. Chapter five is on optimal differential filters on hexagonal lattice. The filters are compared with existing optimised filters to demonstrate the superiority of the technique. Chapter six is on graph image language techniques supporting advanced classification and computer interpretation of 3D CT coronary vessel visualizations. Chapter seven is on a graph matching approach to symmetry detection and analysis. The authors have validated their approach using extensive experiments on two and three dimensional synthetic and real life images. Chapter eight is on pattern classification methods used for the analysis of brain visualization and Computer-aided Diagnosis of perfusion CT maps. The final chapter is on the main methods of multi-class and multi-label classification. These can be applied to a large variety of applications and research fields that relate to human knowledge, cognition and behaviour. We believe that scientists, application engineers, university professors, students, and all interested with this subject readers will find this book useful and interesting
Preface This book would not have existed without the excellent contributions by the emain grateful to the reviewers for their constructive comments. The excellent editorial assistance by the Springer-Verlag is acknowledged. Marek r. ogiela Lakhmi C. Ja australia
VI Preface This book would not have existed without the excellent contributions by the authors. We remain grateful to the reviewers for their constructive comments. The excellent editorial assistance by the Springer-Verlag is acknowledged. Marek R. Ogiela Poland Lakhmi C. Jain Australia
Contents Chapter 1 Recent Advances in Pattern Classification Marek R. Ogiela, Lakhmi C Jain 1 New Directions in Pattern Classification References Chapter 2 Neural Networks for Handwriting Recognition Marcus Liwicki. Alex graves. horst bunke 1 Introduction 1.1 State-of-the-Art 1. 2 Contribution 2 Data processing 2.1 General Processing Steps 2.2 Our Online System 2.3 Our Offline System 3 Neural Network Based Recognition 3.1 Recurrent Neural Networks(RNNs) 3.2 Long Short-Term Memory (LSTM) 3.3 Bidirectional Recurrent Neural Networks 3.4 Connectionist Temporal Classification(CTC) 3.5 Multidimensional Recurrent Neural Networks 3.6 Hierarchical Subsampling Recurrent Neural Networks Experiments…… 4.1 Comparison with HMMs on the IAM Databases.... 4.2 Recognition Performance of MdlStM on Contest'Data 5 Conclusion References Chapter 3 Moving Object Detection from Mobile Platforms Using Stereo Data Angel D Sappa, David Geronimo, Fadi Dornaika, Mohammad Rouhani Antonio M. Lopez 1 Introduction 2 Related Work
Contents Chapter 1 Recent Advances in Pattern Classification..........................................................1 Marek R. Ogiela, Lakhmi C. Jain 1 New Directions in Pattern Classification......................................................1 References .........................................................................................................4 Chapter 2 Neural Networks for Handwriting Recognition..................................................5 Marcus Liwicki, Alex Graves, Horst Bunke 1 Introduction ..................................................................................................5 1.1 State-of-the-Art.....................................................................................6 1.2 Contribution..........................................................................................7 2 Data Processing ............................................................................................8 2.1 General Processing Steps......................................................................9 2.2 Our Online System .............................................................................10 2.3 Our Offline System.............................................................................12 3 Neural Network Based Recognition ...........................................................12 3.1 Recurrent Neural Networks (RNNs)...................................................12 3.2 Long Short-Term Memory (LSTM) ...................................................13 3.3 Bidirectional Recurrent Neural Networks...........................................16 3.4 Connectionist Temporal Classification (CTC) ...................................16 3.5 Multidimensional Recurrent Neural Networks ...................................17 3.6 Hierarchical Subsampling Recurrent Neural Networks......................18 4 Experiments ................................................................................................18 4.1 Comparison with HMMs on the IAM Databases................................18 4.2 Recognition Performance of MDLSTM on Contest’ Data .................20 5 Conclusion ..................................................................................................21 References .......................................................................................................21 Chapter 3 Moving Object Detection from Mobile Platforms Using Stereo Data Registration......................................................................................................... 25 Angel D. Sappa, David Gerónimo, Fadi Dornaika, Mohammad Rouhani, Antonio M. López 1 Introduction ............................................................................................... 25 2 Related Work............................................................................................. 26
Contents 3 Proposed Approach 3.1 System Setup 3.2 Feature Detection and Trackin 3.3 Robust registration 3.4 Frame Subtraction Experimental Results 32 References Chapter 4 Pattern Classifications in Cognitive Informatics Lidia Ogiela 1 Introduction .39 2 Semantic Analysis Stages 3 Semantic Analysis vs. Cognitive Informatics 4 Example of a Cognitive UBIAS System References Optimal Differential Filter on Hexagonal Lattice. Suguru Saito, Masayuki Nakajiama, Tetsuo Shima 2 Preliminaries 60 3 Least Inconsistent Image .60 4 Point Spread Function 5 Condition for gradient Filter 67 6 Numerical Optimization.. 68 7 Theoretical Evaluation 7. 1 Signal-to-Noise Ratio 8 Experimental Evaluation. 8.1 Construction of Artificial Images 8.2 Detection of Gradient Intensity and Orientation 8.3 Overington's Method of Orientation Detection 8.4 Relationship between Derived Filter and Staunton Filter 8.5 Experiment and Results 81 10 Summary. References
VIII Contents 3 Proposed Approach.................................................................................... 28 3.1 System Setup ..................................................................................... 29 3.2 Feature Detection and Tracking......................................................... 29 3.3 Robust Registration ........................................................................... 31 3.4 Frame Subtraction.............................................................................. 31 4 Experimental Results ................................................................................. 34 5 Conclusions ............................................................................................... 35 References ...................................................................................................... 35 Chapter 4 Pattern Classifications in Cognitive Informatics ............................................ 39 Lidia Ogiela 1 Introduction ............................................................................................... 39 2 Semantic Analysis Stages .......................................................................... 40 3 Semantic Analysis vs. Cognitive Informatics ............................................ 43 4 Example of a Cognitive UBIAS System.................................................... 45 5 Conclusions ............................................................................................... 52 References ...................................................................................................... 52 Chapter 5 Optimal Differential Filter on Hexagonal Lattice............................................ 59 Suguru Saito, Masayuki Nakajiama, Tetsuo Shima 1 Introduction ............................................................................................... 59 2 Preliminaries.............................................................................................. 60 3 Least Inconsistent Image ........................................................................... 60 4 Point Spread Function................................................................................ 64 5 Condition for Gradient Filter ..................................................................... 67 6 Numerical Optimization ............................................................................ 68 7 Theoretical Evaluation............................................................................... 70 7.1 Signal-to-Noise Ratio ........................................................................ 70 7.2 Localization ....................................................................................... 73 8 Experimental Evaluation............................................................................ 74 8.1 Construction of Artificial Images ...................................................... 74 8.2 Detection of Gradient Intensity and Orientation................................ 76 8.3 Overington's Method of Orientation Detection.................................. 76 8.4 Relationship between Derived Filter and Staunton Filter .................. 78 8.5 Experiment and Results ..................................................................... 80 9 Discussion.................................................................................................. 81 10 Summary.................................................................................................. 83 References ...................................................................................................... 86
Contents Chapter 6 Graph Image Language Techniques Supporting Advanced Classification and Cognitive Interpretation of CT Coronary Vessel Visualizations 1 Introduction 2 The Classification problem 3 Stages in the Analysis of CT Images under a Structural Approach Utilising Graph Techniques 4 Parsing Languages Generated by Graph Grammars. 5 Picture Grammars in Classification and Semantic Interpretation of 3D Coronary Vessels Visualisations 5.1 Characteristics of the lmage data 5.2 Preliminary Analysis of 3D Coronary Vascularisation 5.3 Graph-Based Linguistic Formalisms in Spatial Modelling of Coronary Vessels 5.4 Detecting Lesions and Constructing the Syntactic Analyser 6 Conclusions Concerning the Advanced Classification and Cognitive 104 5.5 Selected Results Interpretation of CT Coronary Vessel Visualizations 10 Chapter 7 A Graph Matching Approach to Symmetry Detection and analysis Michael chertok and yosi keller 1 Introduction 113 2 Symmetries and Their Properties 115 2.1 Rotational Symmetry 2.2 Reflectional Symmetry 116 2.3 Interrelations between Rotational and Reflectional Symmetries. 117 2.4 Discussion 3 Previous Work 117 3.1 Previous Work in Symmetry Detection and Analysis 118 3.2 Local features 3.3 Spectral Matching of Sets of Points in R Spectral Symmetry Analysis 4.1 Spectral Symmetry Analysis of Sets in R 4. 1. 1 Perfect Symmetry and Spectral De 4.2 Spectral Symmetry Analysis of Images 4.2. 1 Image Representation by Local Features 125 4.2.2 Symmetry Categorization and Pruning 4.2.3 Computing the Geometrical Properties of the Symmetry..126 5 Experimental Results 127 5.1 Symmetry Analysis of Images... 5.2 Statistical Accuracy Analysis 134
Contents IX Chapter 6 Graph Image Language Techniques Supporting Advanced Classification and Cognitive Interpretation of CT Coronary Vessel Visualizations ............ 89 Mirosław Trzupek 1 Introduction ............................................................................................... 89 2 The Classification Problem........................................................................ 92 3 Stages in the Analysis of CT Images under a Structural Approach Utilising Graph Techniques ....................................................................... 93 4 Parsing Languages Generated by Graph Grammars .................................. 95 5 Picture Grammars in Classification and Semantic Interpretation of 3D Coronary Vessels Visualisations ............................................................... 96 5.1 Characteristics of the Image Data ...................................................... 96 5.2 Preliminary Analysis of 3D Coronary Vascularisation Reconstructions.................................................................................. 96 5.3 Graph-Based Linguistic Formalisms in Spatial Modelling of Coronary Vessels ............................................................................... 98 5.4 Detecting Lesions and Constructing the Syntactic Analyser ........... 103 5.5 Selected Results ............................................................................... 104 6 Conclusions Concerning the Advanced Classification and Cognitive Interpretation of CT Coronary Vessel Visualizations............................. 108 References .................................................................................................... 110 Chapter 7 A Graph Matching Approach to Symmetry Detection and Analysis............113 Michael Chertok and Yosi Keller 1 Introduction ..............................................................................................113 2 Symmetries and Their Properties..............................................................115 2.1 Rotational Symmetry ........................................................................115 2.2 Reflectional Symmetry .....................................................................116 2.3 Interrelations between Rotational and Reflectional Symmetries ......117 2.4 Discussion.........................................................................................117 3 Previous Work ..........................................................................................117 3.1 Previous Work in Symmetry Detection and Analysis.......................118 3.2 Local Features...................................................................................121 3.3 Spectral Matching of Sets of Points in Rn .........................................122 4 Spectral Symmetry Analysis.....................................................................123 4.1 Spectral Symmetry Analysis of Sets in Rn .......................................123 4.1.1 Perfect Symmetry and Spectral Degeneracy..........................124 4.2 Spectral Symmetry Analysis of Images ............................................124 4.2.1 Image Representation by Local Features ...............................125 4.2.2 Symmetry Categorization and Pruning ..................................125 4.2.3 Computing the Geometrical Properties of the Symmetry ......126 5 Experimental Results ................................................................................127 5.1 Symmetry Analysis of Images ..........................................................128 5.2 Statistical Accuracy Analysis ...........................................................134
Contents 5.3 Analysis of Three-Dimensional Symmetry. 5.5 Additional Results 140 6 Conclusions 140 References Chapter 8 Pattern Classification Methods for Analysis and visualization of Brain Perfusion CT Maps 145 Hach 1 Introduction 2 Interpretation of Perfusion Maps-Long and Short Time Prognosis.. 148 3 Image Processing and Abnormality Detection 4 Image Registration 153 4.1 Affine Registration 4.2 FFD Registration 4.3 Thirion's Demons algorithm 154 4.4 Comparison of Registration Algorithms 5 Classification of Detected Abnormalities 6 System Validation and Results 7 Data Visualization- Augmented Reality Environment 162 7.1 Augmented Reality Environment 7.2 Real Time Rendering of 3D Data 7.3 Augmented Desktop-System Performance Test 164 8 Summary. References 168 Chapter 9 Inference of Co-occurring Classes: Multi-class and Multi-label Classification 171 Tal Sobol-Shikle 1 Introduction 2 Applications 3 The Classification Process 173 4 Data and Annotation .175 5 Classification Approaches 5.1 Binary Classification 5.2 Multi-class Classification 177 5.3 Multi-label Classification 6 Multi-class Classification 6. 1 Multiple Binary Classifiers 6.1.1 One-Against-All Classification 6.1.2 One-Against-One(Pair-Wise)Classification 6.1.3 Combining Binary Classifiers
X Contents 5.3 Analysis of Three-Dimensional Symmetry.......................................136 5.4 Implementation Issues ......................................................................137 5.5 Additional Results ............................................................................140 6 Conclusions ..............................................................................................140 References .....................................................................................................142 Chapter 8 Pattern Classification Methods for Analysis and Visualization of Brain Perfusion CT Maps............................................................................................145 Tomasz Hachaj 1 Introduction ..............................................................................................145 2 Interpretation of Perfusion Maps – Long and Short Time Prognosis........148 3 Image Processing and Abnormality Detection..........................................150 4 Image Registration....................................................................................153 4.1 Affine Registration ...........................................................................154 4.2 FFD Registration ..............................................................................154 4.3 Thirion’s Demons Algorithm............................................................154 4.4 Comparison of Registration Algorithms ...........................................155 5 Classification of Detected Abnormalities .................................................158 6 System Validation and Results .................................................................160 7 Data Visualization – Augmented Reality Environment............................162 7.1 Augmented Reality Environment .....................................................163 7.2 Real Time Rendering of 3D Data .....................................................164 7.3 Augmented Desktop - System Performance Test .............................164 8 Summary...................................................................................................167 References .....................................................................................................168 Chapter 9 Inference of Co-occurring Classes: Multi-class and Multi-label Classification ......................................................................................................171 Tal Sobol-Shikler 1 Introduction ..............................................................................................171 2 Applications..............................................................................................172 3 The Classification Process ........................................................................173 4 Data and Annotation .................................................................................175 5 Classification Approaches ........................................................................177 5.1 Binary Classification.........................................................................177 5.2 Multi-class Classification .................................................................177 5.3 Multi-label Classification .................................................................178 6 Multi-class Classification .........................................................................179 6.1 Multiple Binary Classifiers...............................................................180 6.1.1 One-Against-All Classification..............................................180 6.1.2 One-Against-One (Pair-Wise) Classification.........................180 6.1.3 Combining Binary Classifiers................................................181
Contents 6.2 Direct Multi-class Classification 6.3 Associative Classification 7 Multi-label Classification 7.1 Semi-supervised(Annotation) Methods 8 Inference of Co-occurring Affective States from Non-verbal Speech 9 Summary References Author Index
Contents XI 6.2 Direct Multi-class Classification.......................................................181 6.3 Associative Classification.................................................................182 7 Multi-label Classification .........................................................................182 7.1 Semi-supervised (Annotation) Methods ...........................................186 8 Inference of Co-occurring Affective States from Non-verbal Speech......186 9 Summary...................................................................................................193 References .....................................................................................................193 Author Index......................................................................................................199
