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Modern machine learning techniques and their applications in cartoon animation research / / Jun Yu, Dacheng Tao
| Modern machine learning techniques and their applications in cartoon animation research / / Jun Yu, Dacheng Tao |
| Autore | Yu Jun |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : IEEE Press, , c2013 |
| Descrizione fisica | 1 online resource (210 p.) |
| Disciplina |
006.6/96
006.696 |
| Altri autori (Persone) | TaoDacheng <1978-> |
| Collana | IEEE Press Series on Systems Science and Engineering |
| Soggetto topico |
Computer animation
Machine learning |
| ISBN |
1-299-44909-3
1-118-55998-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xi -- 1 Introduction 1 -- 1.1 Perception 2 -- 1.2 Overview of Machine Learning Techniques 2 -- 1.2.1 Manifold Learning 3 -- 1.2.2 Semi-supervised Learning 5 -- 1.2.3 Multiview Learning 8 -- 1.2.4 Learning-based Optimization 9 -- 1.3 Recent Developments in Computer Animation 11 -- 1.3.1 Example-Based Motion Reuse 11 -- 1.3.2 Physically Based Computer Animation 26 -- 1.3.3 Computer-Assisted Cartoon Animation 33 -- 1.3.4 Crowd Animation 42 -- 1.3.5 Facial Animation 51 -- 1.4 Chapter Summary 60 -- 2 Modern Machine Learning Techniques 63 -- 2.1 A Unified Framework for Manifold Learning 65 -- 2.1.1 Framework Introduction 65 -- 2.1.2 Various Manifold Learning Algorithm Unifying 67 -- 2.1.3 Discriminative Locality Alignment 69 -- 2.1.4 Discussions 71 -- 2.2 Spectral Clustering and Graph Cut 71 -- 2.2.1 Spectral Clustering 72 -- 2.2.2 Graph Cut Approximation 76 -- 2.3 Ensemble Manifold Learning 81 -- 2.3.1 Motivation for EMR 81 -- 2.3.2 Overview of EMR 81 -- 2.3.3 Applications of EMR 84 -- 2.4 Multiple Kernel Learning 86 -- 2.4.1 A Unified Mulitple Kernel Learning Framework 87 -- 2.4.2 SVM with Multiple Unweighted-Sum Kernels 89 -- 2.4.3 QCQP Multiple Kernel Learning 89 -- 2.5 Multiview Subspace Learning 90 -- 2.5.1 Approach Overview 90 -- 2.5.2 Techinique Details 90 -- 2.5.3 Alternative Optimization Used in PA-MSL 93 -- 2.6 Multiview Distance Metric Learning 94 -- 2.6.1 Motivation for MDML 94 -- 2.6.2 Graph-Based Semi-supervised Learning 95 -- 2.6.3 Overview of MDML 95 -- 2.7 Multi-task Learning 98 -- 2.7.1 Introduction of Structural Learning 99 -- 2.7.2 Hypothesis Space Selection 100 -- 2.7.3 Algorithm for Multi-task Learning 101 -- 2.7.4 Solution by Alternative Optimization 102 -- 2.8 Chapter Summary 103 -- 3 Animation Research: A Brief Introduction 105 -- 3.1 Traditional Animation Production 107 -- 3.1.1 History of Traditional Animation Production 107 -- 3.1.2 Procedures of Animation Production 108 -- 3.1.3 Relationship Between Traditional Animation and Computer Animation 109.
3.2 Computer-Assisted Systems 110 -- 3.2.1 Computer Animation Techniques 111 -- 3.3 Cartoon Reuse Systems for Animation Synthesis 117 -- 3.3.1 Cartoon Texture for Animation Synthesis 118 -- 3.3.2 Cartoon Motion Reuse 120 -- 3.3.3 Motion Capture Data Reuse in Cartoon Characters 122 -- 3.4 Graphical Materials Reuse: More Examples 124 -- 3.4.1 Video Clips Reuse 124 -- 3.4.2 Motion Captured Data Reuse by Motion Texture 126 -- 3.4.3 Motion Capture Data Reuse by Motion Graph 127 -- 3.5 Chapter Summary 129 -- 4 Animation Research: Modern Techniques 131 -- 4.1 Automatic Cartoon Generation with Correspondence Construction 131 -- 4.1.1 Related Work in Correspondence Construction 132 -- 4.1.2 Introduction of the Semi-supervised Correspondence Construction 133 -- 4.1.3 Stroke Correspondence Construction via Stroke Reconstruction Algorithm 138 -- 4.1.4 Simulation Results 141 -- 4.2 Cartoon Characters Represented by Multiple Features 146 -- 4.2.1 Cartoon Character Extraction 147 -- 4.2.2 Color Histogram 148 -- 4.2.3 Hausdorff Edge Feature 148 -- 4.2.4 Motion Feature 150 -- 4.2.5 Skeleton Feature 151 -- 4.2.6 Complementary Characteristics of Multiview Features 153 -- 4.3 Graph-based Cartoon Clips Synthesis 154 -- 4.3.1 Graph Model Construction 155 -- 4.3.2 Distance Calculation 155 -- 4.3.3 Simulation Results 156 -- 4.4 Retrieval-based Cartoon Clips Synthesis 161 -- 4.4.1 Constrained Spreading Activation Network 162 -- 4.4.2 Semi-supervised Multiview Subspace Learning 165 -- 4.4.3 Simulation Results 168 -- 4.5 Chapter Summary 173 -- References 174 -- Index 195. |
| Record Nr. | UNINA-9910139027603321 |
Yu Jun
|
||
| Piscataway, New Jersey : , : IEEE Press, , c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modern machine learning techniques and their applications in cartoon animation research / / Jun Yu, Dacheng Tao
| Modern machine learning techniques and their applications in cartoon animation research / / Jun Yu, Dacheng Tao |
| Autore | Yu Jun |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : IEEE Press, , c2013 |
| Descrizione fisica | 1 online resource (210 p.) |
| Disciplina |
006.6/96
006.696 |
| Altri autori (Persone) | TaoDacheng <1978-> |
| Collana | IEEE Press Series on Systems Science and Engineering |
| Soggetto topico |
Computer animation
Machine learning |
| ISBN |
1-299-44909-3
1-118-55998-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xi -- 1 Introduction 1 -- 1.1 Perception 2 -- 1.2 Overview of Machine Learning Techniques 2 -- 1.2.1 Manifold Learning 3 -- 1.2.2 Semi-supervised Learning 5 -- 1.2.3 Multiview Learning 8 -- 1.2.4 Learning-based Optimization 9 -- 1.3 Recent Developments in Computer Animation 11 -- 1.3.1 Example-Based Motion Reuse 11 -- 1.3.2 Physically Based Computer Animation 26 -- 1.3.3 Computer-Assisted Cartoon Animation 33 -- 1.3.4 Crowd Animation 42 -- 1.3.5 Facial Animation 51 -- 1.4 Chapter Summary 60 -- 2 Modern Machine Learning Techniques 63 -- 2.1 A Unified Framework for Manifold Learning 65 -- 2.1.1 Framework Introduction 65 -- 2.1.2 Various Manifold Learning Algorithm Unifying 67 -- 2.1.3 Discriminative Locality Alignment 69 -- 2.1.4 Discussions 71 -- 2.2 Spectral Clustering and Graph Cut 71 -- 2.2.1 Spectral Clustering 72 -- 2.2.2 Graph Cut Approximation 76 -- 2.3 Ensemble Manifold Learning 81 -- 2.3.1 Motivation for EMR 81 -- 2.3.2 Overview of EMR 81 -- 2.3.3 Applications of EMR 84 -- 2.4 Multiple Kernel Learning 86 -- 2.4.1 A Unified Mulitple Kernel Learning Framework 87 -- 2.4.2 SVM with Multiple Unweighted-Sum Kernels 89 -- 2.4.3 QCQP Multiple Kernel Learning 89 -- 2.5 Multiview Subspace Learning 90 -- 2.5.1 Approach Overview 90 -- 2.5.2 Techinique Details 90 -- 2.5.3 Alternative Optimization Used in PA-MSL 93 -- 2.6 Multiview Distance Metric Learning 94 -- 2.6.1 Motivation for MDML 94 -- 2.6.2 Graph-Based Semi-supervised Learning 95 -- 2.6.3 Overview of MDML 95 -- 2.7 Multi-task Learning 98 -- 2.7.1 Introduction of Structural Learning 99 -- 2.7.2 Hypothesis Space Selection 100 -- 2.7.3 Algorithm for Multi-task Learning 101 -- 2.7.4 Solution by Alternative Optimization 102 -- 2.8 Chapter Summary 103 -- 3 Animation Research: A Brief Introduction 105 -- 3.1 Traditional Animation Production 107 -- 3.1.1 History of Traditional Animation Production 107 -- 3.1.2 Procedures of Animation Production 108 -- 3.1.3 Relationship Between Traditional Animation and Computer Animation 109.
3.2 Computer-Assisted Systems 110 -- 3.2.1 Computer Animation Techniques 111 -- 3.3 Cartoon Reuse Systems for Animation Synthesis 117 -- 3.3.1 Cartoon Texture for Animation Synthesis 118 -- 3.3.2 Cartoon Motion Reuse 120 -- 3.3.3 Motion Capture Data Reuse in Cartoon Characters 122 -- 3.4 Graphical Materials Reuse: More Examples 124 -- 3.4.1 Video Clips Reuse 124 -- 3.4.2 Motion Captured Data Reuse by Motion Texture 126 -- 3.4.3 Motion Capture Data Reuse by Motion Graph 127 -- 3.5 Chapter Summary 129 -- 4 Animation Research: Modern Techniques 131 -- 4.1 Automatic Cartoon Generation with Correspondence Construction 131 -- 4.1.1 Related Work in Correspondence Construction 132 -- 4.1.2 Introduction of the Semi-supervised Correspondence Construction 133 -- 4.1.3 Stroke Correspondence Construction via Stroke Reconstruction Algorithm 138 -- 4.1.4 Simulation Results 141 -- 4.2 Cartoon Characters Represented by Multiple Features 146 -- 4.2.1 Cartoon Character Extraction 147 -- 4.2.2 Color Histogram 148 -- 4.2.3 Hausdorff Edge Feature 148 -- 4.2.4 Motion Feature 150 -- 4.2.5 Skeleton Feature 151 -- 4.2.6 Complementary Characteristics of Multiview Features 153 -- 4.3 Graph-based Cartoon Clips Synthesis 154 -- 4.3.1 Graph Model Construction 155 -- 4.3.2 Distance Calculation 155 -- 4.3.3 Simulation Results 156 -- 4.4 Retrieval-based Cartoon Clips Synthesis 161 -- 4.4.1 Constrained Spreading Activation Network 162 -- 4.4.2 Semi-supervised Multiview Subspace Learning 165 -- 4.4.3 Simulation Results 168 -- 4.5 Chapter Summary 173 -- References 174 -- Index 195. |
| Record Nr. | UNISA-996202754503316 |
|
Yu Jun
|
||
| Piscataway, New Jersey : , : IEEE Press, , c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modern machine learning techniques and their applications in cartoon animation research / / Jun Yu, Dacheng Tao
| Modern machine learning techniques and their applications in cartoon animation research / / Jun Yu, Dacheng Tao |
| Autore | Yu Jun |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : IEEE Press, , c2013 |
| Descrizione fisica | 1 online resource (210 p.) |
| Disciplina |
006.6/96
006.696 |
| Altri autori (Persone) | TaoDacheng <1978-> |
| Collana | IEEE Press Series on Systems Science and Engineering |
| Soggetto topico |
Computer animation
Machine learning |
| ISBN |
1-299-44909-3
1-118-55998-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xi -- 1 Introduction 1 -- 1.1 Perception 2 -- 1.2 Overview of Machine Learning Techniques 2 -- 1.2.1 Manifold Learning 3 -- 1.2.2 Semi-supervised Learning 5 -- 1.2.3 Multiview Learning 8 -- 1.2.4 Learning-based Optimization 9 -- 1.3 Recent Developments in Computer Animation 11 -- 1.3.1 Example-Based Motion Reuse 11 -- 1.3.2 Physically Based Computer Animation 26 -- 1.3.3 Computer-Assisted Cartoon Animation 33 -- 1.3.4 Crowd Animation 42 -- 1.3.5 Facial Animation 51 -- 1.4 Chapter Summary 60 -- 2 Modern Machine Learning Techniques 63 -- 2.1 A Unified Framework for Manifold Learning 65 -- 2.1.1 Framework Introduction 65 -- 2.1.2 Various Manifold Learning Algorithm Unifying 67 -- 2.1.3 Discriminative Locality Alignment 69 -- 2.1.4 Discussions 71 -- 2.2 Spectral Clustering and Graph Cut 71 -- 2.2.1 Spectral Clustering 72 -- 2.2.2 Graph Cut Approximation 76 -- 2.3 Ensemble Manifold Learning 81 -- 2.3.1 Motivation for EMR 81 -- 2.3.2 Overview of EMR 81 -- 2.3.3 Applications of EMR 84 -- 2.4 Multiple Kernel Learning 86 -- 2.4.1 A Unified Mulitple Kernel Learning Framework 87 -- 2.4.2 SVM with Multiple Unweighted-Sum Kernels 89 -- 2.4.3 QCQP Multiple Kernel Learning 89 -- 2.5 Multiview Subspace Learning 90 -- 2.5.1 Approach Overview 90 -- 2.5.2 Techinique Details 90 -- 2.5.3 Alternative Optimization Used in PA-MSL 93 -- 2.6 Multiview Distance Metric Learning 94 -- 2.6.1 Motivation for MDML 94 -- 2.6.2 Graph-Based Semi-supervised Learning 95 -- 2.6.3 Overview of MDML 95 -- 2.7 Multi-task Learning 98 -- 2.7.1 Introduction of Structural Learning 99 -- 2.7.2 Hypothesis Space Selection 100 -- 2.7.3 Algorithm for Multi-task Learning 101 -- 2.7.4 Solution by Alternative Optimization 102 -- 2.8 Chapter Summary 103 -- 3 Animation Research: A Brief Introduction 105 -- 3.1 Traditional Animation Production 107 -- 3.1.1 History of Traditional Animation Production 107 -- 3.1.2 Procedures of Animation Production 108 -- 3.1.3 Relationship Between Traditional Animation and Computer Animation 109.
3.2 Computer-Assisted Systems 110 -- 3.2.1 Computer Animation Techniques 111 -- 3.3 Cartoon Reuse Systems for Animation Synthesis 117 -- 3.3.1 Cartoon Texture for Animation Synthesis 118 -- 3.3.2 Cartoon Motion Reuse 120 -- 3.3.3 Motion Capture Data Reuse in Cartoon Characters 122 -- 3.4 Graphical Materials Reuse: More Examples 124 -- 3.4.1 Video Clips Reuse 124 -- 3.4.2 Motion Captured Data Reuse by Motion Texture 126 -- 3.4.3 Motion Capture Data Reuse by Motion Graph 127 -- 3.5 Chapter Summary 129 -- 4 Animation Research: Modern Techniques 131 -- 4.1 Automatic Cartoon Generation with Correspondence Construction 131 -- 4.1.1 Related Work in Correspondence Construction 132 -- 4.1.2 Introduction of the Semi-supervised Correspondence Construction 133 -- 4.1.3 Stroke Correspondence Construction via Stroke Reconstruction Algorithm 138 -- 4.1.4 Simulation Results 141 -- 4.2 Cartoon Characters Represented by Multiple Features 146 -- 4.2.1 Cartoon Character Extraction 147 -- 4.2.2 Color Histogram 148 -- 4.2.3 Hausdorff Edge Feature 148 -- 4.2.4 Motion Feature 150 -- 4.2.5 Skeleton Feature 151 -- 4.2.6 Complementary Characteristics of Multiview Features 153 -- 4.3 Graph-based Cartoon Clips Synthesis 154 -- 4.3.1 Graph Model Construction 155 -- 4.3.2 Distance Calculation 155 -- 4.3.3 Simulation Results 156 -- 4.4 Retrieval-based Cartoon Clips Synthesis 161 -- 4.4.1 Constrained Spreading Activation Network 162 -- 4.4.2 Semi-supervised Multiview Subspace Learning 165 -- 4.4.3 Simulation Results 168 -- 4.5 Chapter Summary 173 -- References 174 -- Index 195. |
| Record Nr. | UNINA-9910830655403321 |
|
Yu Jun
|
||
| Piscataway, New Jersey : , : IEEE Press, , c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modern machine learning techniques and their applications in cartoon animation research / / Jun Yu, Dacheng Tao
| Modern machine learning techniques and their applications in cartoon animation research / / Jun Yu, Dacheng Tao |
| Autore | Yu Jun |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Piscataway, N.J., : IEEE Press/Wiley, 2013 |
| Descrizione fisica | 1 online resource (210 p.) |
| Disciplina | 006.6/96 |
| Altri autori (Persone) | TaoDacheng <1978-> |
| Collana | IEEE Press series on systems science and engineering |
| Soggetto topico |
Machine learning
Computer animation |
| ISBN |
1-299-44909-3
1-118-55998-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xi -- 1 Introduction 1 -- 1.1 Perception 2 -- 1.2 Overview of Machine Learning Techniques 2 -- 1.2.1 Manifold Learning 3 -- 1.2.2 Semi-supervised Learning 5 -- 1.2.3 Multiview Learning 8 -- 1.2.4 Learning-based Optimization 9 -- 1.3 Recent Developments in Computer Animation 11 -- 1.3.1 Example-Based Motion Reuse 11 -- 1.3.2 Physically Based Computer Animation 26 -- 1.3.3 Computer-Assisted Cartoon Animation 33 -- 1.3.4 Crowd Animation 42 -- 1.3.5 Facial Animation 51 -- 1.4 Chapter Summary 60 -- 2 Modern Machine Learning Techniques 63 -- 2.1 A Unified Framework for Manifold Learning 65 -- 2.1.1 Framework Introduction 65 -- 2.1.2 Various Manifold Learning Algorithm Unifying 67 -- 2.1.3 Discriminative Locality Alignment 69 -- 2.1.4 Discussions 71 -- 2.2 Spectral Clustering and Graph Cut 71 -- 2.2.1 Spectral Clustering 72 -- 2.2.2 Graph Cut Approximation 76 -- 2.3 Ensemble Manifold Learning 81 -- 2.3.1 Motivation for EMR 81 -- 2.3.2 Overview of EMR 81 -- 2.3.3 Applications of EMR 84 -- 2.4 Multiple Kernel Learning 86 -- 2.4.1 A Unified Mulitple Kernel Learning Framework 87 -- 2.4.2 SVM with Multiple Unweighted-Sum Kernels 89 -- 2.4.3 QCQP Multiple Kernel Learning 89 -- 2.5 Multiview Subspace Learning 90 -- 2.5.1 Approach Overview 90 -- 2.5.2 Techinique Details 90 -- 2.5.3 Alternative Optimization Used in PA-MSL 93 -- 2.6 Multiview Distance Metric Learning 94 -- 2.6.1 Motivation for MDML 94 -- 2.6.2 Graph-Based Semi-supervised Learning 95 -- 2.6.3 Overview of MDML 95 -- 2.7 Multi-task Learning 98 -- 2.7.1 Introduction of Structural Learning 99 -- 2.7.2 Hypothesis Space Selection 100 -- 2.7.3 Algorithm for Multi-task Learning 101 -- 2.7.4 Solution by Alternative Optimization 102 -- 2.8 Chapter Summary 103 -- 3 Animation Research: A Brief Introduction 105 -- 3.1 Traditional Animation Production 107 -- 3.1.1 History of Traditional Animation Production 107 -- 3.1.2 Procedures of Animation Production 108 -- 3.1.3 Relationship Between Traditional Animation and Computer Animation 109.
3.2 Computer-Assisted Systems 110 -- 3.2.1 Computer Animation Techniques 111 -- 3.3 Cartoon Reuse Systems for Animation Synthesis 117 -- 3.3.1 Cartoon Texture for Animation Synthesis 118 -- 3.3.2 Cartoon Motion Reuse 120 -- 3.3.3 Motion Capture Data Reuse in Cartoon Characters 122 -- 3.4 Graphical Materials Reuse: More Examples 124 -- 3.4.1 Video Clips Reuse 124 -- 3.4.2 Motion Captured Data Reuse by Motion Texture 126 -- 3.4.3 Motion Capture Data Reuse by Motion Graph 127 -- 3.5 Chapter Summary 129 -- 4 Animation Research: Modern Techniques 131 -- 4.1 Automatic Cartoon Generation with Correspondence Construction 131 -- 4.1.1 Related Work in Correspondence Construction 132 -- 4.1.2 Introduction of the Semi-supervised Correspondence Construction 133 -- 4.1.3 Stroke Correspondence Construction via Stroke Reconstruction Algorithm 138 -- 4.1.4 Simulation Results 141 -- 4.2 Cartoon Characters Represented by Multiple Features 146 -- 4.2.1 Cartoon Character Extraction 147 -- 4.2.2 Color Histogram 148 -- 4.2.3 Hausdorff Edge Feature 148 -- 4.2.4 Motion Feature 150 -- 4.2.5 Skeleton Feature 151 -- 4.2.6 Complementary Characteristics of Multiview Features 153 -- 4.3 Graph-based Cartoon Clips Synthesis 154 -- 4.3.1 Graph Model Construction 155 -- 4.3.2 Distance Calculation 155 -- 4.3.3 Simulation Results 156 -- 4.4 Retrieval-based Cartoon Clips Synthesis 161 -- 4.4.1 Constrained Spreading Activation Network 162 -- 4.4.2 Semi-supervised Multiview Subspace Learning 165 -- 4.4.3 Simulation Results 168 -- 4.5 Chapter Summary 173 -- References 174 -- Index 195. |
| Record Nr. | UNINA-9910877474703321 |
|
Yu Jun
|
||
| Piscataway, N.J., : IEEE Press/Wiley, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||