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Part I Introduction
1 Computer Science and Fine Arts 3
1.1 Why Use Computers for Arts? 3
1.1.1 Computer as an Art Tool 3
1.1.2 Computer as an Exceptional Art Tool 5
1.1.3 Computers as Mind-talkers 5
1.2 Digital Arts 7
1.2.1 What Are Digital Arts? 7
1.2.2 Manual or Automatic Art Creation 7
1.2.3 Three Elements of Digital Arts 8
1.2.4 Classification of the Book Chapters 9
1.3 Examples of Digital Arts 9
1.3.1 Digital Film 10
1.3.2 Digital Painting 10
1.3.3 Computer Music 10
1.3.4 Digital Sculpture 11
1.3.5 Computer Dance 12
1.3.6 Computer Puppetry 13
1.3.7 Computer Calligraphy 14
1.4 Why Digital Arts Are Computationally Challenging? 15
1.4.1 Lack of Semantic Understanding 15
1.4.2 The Versatile Nature of Art 15
1.4.3 Aesthetic Evaluation and Feedback 16
1.4.4 Inhomogeneity between the Two Types of Intelligence 16
References 17
Part II Computer Science in Painting: A Brief Survey
2 Computer Science in Paintings or Drawings 23
2.1 Introduction 23
2.2 Automatic Generation of Paintings and Drawings from Photographs 23
2.2.1 Early Pioneering Work 23
2.2.2 Representative Recent Work 25
2.2.3 Generating Paintings via Human-computer Interaction 29
2.3 Automatic Generation of Painterly Rendering Animation from Videos 30
2.4 Interactive Generation of Painterly Rendering Images 31
2.5 Automatic Generation of Painterly Rendering from 3D Models 32
2.5.1 Automatic Generation of Illustrations and Line Drawings from 3D Models 32
2.5.2 Generating Painterly Rendering Animations from 3D Models 35
2.5.3 Domain Specific Special-purpose Painterly Rendition Generation 36
2.5.4 Efficient Painterly Rendition Generation 39
2.6 SpecialSupport for Digital Painting 40
2.6.1 Hardware Support for Digital Painting 40
2.6.2 Multiresolutional Painting 40
References 42
Part III Interactive Digital Painting and Calligraphy
3 Introduction to Interactive Digital Chinese Painting and Calligraphy 51
3.1 Overview 51
3.2 Background 51
3.2.1 Previous Work 52
3.2.2 Our Virtual Brush 55
References 56
4 Basic Algorithmic Framework of a Virtual Hairy Paintbrush System 59
4.1 Overview 59
4.2 Introduction 59
4.2.1 Overview of E-brush and Related Research 60
4.2.2 Our Work and Contributions 62
4.3 Writing Primitives 64
4.4 The Model and the States 65
4.4.1 The Parametric Model of the Virtual Hairy Brush 65
4.4.2 The Parametric Model of a Writing Primitive 67
4.4.3 The Three States of a Brush 70
4.5 Sampling of the Input Data 72
4.6 Dynamic Adjustments of the Brush 75
4.6.1 Estimating the Pysical Conditions of the Brush 75
4.6.2 Dynamic Adjustment of the Middle Control Axis 76
4.6.3 Dynamic Adjustment of the Middle Control Ellipse 78
4.6.4 Dynamic Adjustment of the Tip Control Line 79
4.6.5 Splitting of the Virtual Hairy Brush 80
4.6.6 Ink Flowage between Writing Primitives 80
4.7 The Writing Process 81
4.8 Customizing the Brush 83
4.8.1 Quality Parameters 83
4.8.2 Configuring the Brush with Machine Intelligence 84
4.9 System Implementation and Experiment Results 87
4.10 Related Work 89
4.10.1 DAB 90
4.10.2 Virtual Brush by Wong and Ip 93
4.10.3 Other Virtual Brush Models 94
4.11 Conclusion and Future Work 94
4.11.1 Summary and Conclusion 94
4.11.2 Future Work 95
References 97
5 Performance Enhanced Virtual Hairy Paintbrush System 103
5.1 Overview 103
5.2 Introduction 103
5.3 Modeling the Paintbrush's Geometry 105
5.3.1 Three-layer Hierarchical Modeling 106
5.3.2 Real-time Visual Display of the Brush 108
5.4 Modeling the Paintbrush's Dynamic Behavior 110
5.4.1 Deformation due to Brush-paper Collision 112
5.4.2 Deformation due to Inner Stress 114
5.4.3 Calibrating the On-line Results 117
5.5 E-painting System based on Realistic Virtual Brush Modeling 119
5.5.1 Additional Components of Our New Painting System 119
5.5.2 The Running System 121
5.6 Related Work 121
5.6.1 Wong & Ip's System 121
5.6.2 The DAB System 123
5.6.3 Chu & Tai's System 124
5.7 Conclusion and Future Work 125
References 126
6 Pigment Component of an Advanced Virtual Hairy Paintbrush System 129
6.1 Overview 129
6.2 Introduction 129
6.2.1 Main Ideas 130
6.2.2 Pigment Model and the Brush 131
6.2.3 Organization of the Chapter 132
6.3 Previous Work 133
6.3.1 Pigment Behavior Models 133
6.3.2 Comparison with Chu & Tai's Work 134
6.4 Pigment Sorption between the Brush and the Paper Surface 135
6.5 Pigment Diffusion on the Paper Surface 137
6.6 Pigment Diffusion at the Brush Tip 139
6.7 Evaporation 141
6.7.1 At the Brush Tip Bundle 141
6.7.2 On the Paper Surface 141
6.8 Pigment Deposition on the Paper Fibers 142
6.9 Rendering the Simulation Results 143
6.9.1 Pigment Mixing with High Fidelity 143
6.9.2 Superimposing the Layers 147
6.10 Hardware-Accelerated Implementation 148
6.11 Experiment Results 148
6.12 Conclusion and Future Work 153
References 154
7 Rendering Component of an Advanced Virtual Hairy Paintbrush System 159
7.1 Motivation 159
7.1.1 Necessity and Importance of Brush Hair Rendering 159
7.1.2 Performance Requirements 160
7.1.3 Brush Hair versus Human Hair 160
Overview 161
7.3 Introduction 161
7.3.1 Ideas and Contributions 161
7.3.2 Organization of the Chapter 162
7.4 Related Work 162
7.4.1 Hair Rendering for Quality 162
7.4.2 Hair Rendering for Speed 163
7.4.3 Image-Based Rendering 163
7.4.4 Appearance Modeling 164
7.5 Hair Modeling and Representation 165
7.5.1 Modeling Hair as Virtual Material 165
7.5.2 Four-level Hierarchy of Hair Modeling 165
7.5.3 Generalized Disk Structure for Representing Hair Clusters 166
7.5.4 Hair Density Field for Sector 168
7.6 HRIR-DB and Semantics-Aware Texture Function 168
7.6.1 SATF and Our Offline/Online Two-phased Rendering Algorithm 168
7.6.2 Minimizing the Size of HRIR-DB 170
7.7 Constructing the Database of Hair Rendering Intermediate Results 171
7.7.1 Deriving an HRIR Record 171
7.7.2 Indexing an HRIR Record 172
7.8 Fast and High Quality Online Hair Rendering 173
7.8.1 Main Steps of Online Hair Rendering 173
7.8.2 SATF and re- and alpha-map Construction 174
7.8.3 Online Hair Lighting 177
7.8.4 Online Hair Self-shadowing 178
7.8.5 Deriving Shading through Integrating All the Rendering Effects Together 179
7.8.6 Hardware Acceleration 180
7.9 Experiment Results 182
7.10 Conclusion and Future Work 190
7.10.1 Conclusion 190
7.10.2 Discussion and Future Work 190
References 195
Part IV Automatic Generation of Artistic Chinese Calligraphy
8 Principles of Automatic Generation of Artistic Chinese Calligraphy 203
8.1 Overview 203
8.2 Introduction 203
8.3 Problem Formulation and Overall System Architecture 206
8.4 Hierarchical and Parametric Representation 208
8.4.1 Hierarchical Representation 208
8.4.2 Six Levels of Parametric Representation 208
8.4.3 Advantages of Our Representation 210
8.5 Calligraphic Shape Decomposition 210
8.5.1 Extracting Levels 0-1 Elements 211
8.5.2 Extracting Levels 2-3 Elements 211
8.5.3 Extracting Level 4 Elements 212
8.6 Calligraphic Model Creation from Examples 212
8.6.1 Principles of Calligraphic Model Creation 212
8.6.2 Fusing Knowledge Sources in ARP 213
8.6.3 A Computational Psychology Perspective 214
8.7 Generating Artistic Calligraphy 214
8.7.1 Extracting Aesthetic Constraints from Training Examples 214
8.7.2 Past Results Reuse for Efficient Reasoning 215
8.8 Experiment Results 216
8.9 Possible Applications 220
8.10 Conclusion and Future Work 222
8.10.1 Conclusion 222
8.10.2 Future Work 222
References 224
9 Two Perspectives on Automatic Generation of Artistic Chinese Calligraphy 227
9.1 Overview 227
9.2 A System Engineering Perspective on Automatic Generation of Artistic Chinese Calligraphy 227
9.3 Hierarchical and Parametric Representation 228
9.3.1 Hierarchical Representation 228
9.3.2 Six Levels of Parametric Representation 229
9.3.3 Deriving Parametric Representations for Constructive Elements 230
9.4 Facsimiling Existent Calligraphy 233
9.4.1 Extracting Levels 0-1 Elements 233
9.4.2 Extracting Levels 2-3 Elements 234
9.4.3 Extracting Level 4 Elements 235
9.5 Generating New Calligraphy 235
9.5.1 Principle of New Calligraphy Generation 235
9.5.2 New Calligraphy Generation System 236
9.6 Generating Artistic Calligraphy 240
9.6.1 Constraints on the Process 240
9.6.2 Extracting Aesthetic Constraints from Existent Artwork 240
9.6.3 Constraint Satisfaction for Calligraphy Generation 241
9.6.4 Relaxing the Aesthetic Constraints 242
9.7 An Artificial Intelligence's Perspective on Automatic Generation of Artistic Chinese Calligraphy 242
9.8 Background 243
9.9 The Synthesis Reasoning Model 243
9.9.1 Features of the Model 244
9.9.2 Key Concepts of the Model 244
9.9.3 The Computational Model of Synthesis Reasoning 245
9.10 A Generic Methodology to Developing Synthesis Reasoning-based Intelligent Systems 248
References 249
10 A Preliminary Attempt at Evaluating the Beauty of Chinese Calligraphy 253
10.1 Overview 253
10.2 Introduction 254
10.2.1 Motivation 254
10.2.2 Chapter Organization 255
10.3 Previous Work 256
10.4 Calligraphy Representation 257
10.5 Extracting Calligraphy Representation through a Two-phased Method 258
10.5.1 Best-effort Automatic Stroke Extraction 258
10.5.2 Intelligent User Interface for the Difficult Cases 265
10.6 Calligraphy Aesthetics Evaluation 267
10.6.1 Evaluating Shapes of Individual Strokes 268
10.6.2 Evaluating Spatial Layout of Strokes 271
10.6.3 Evaluating Coherence of Stroke Styles 274
10.6.4 The Overall Evaluation 275
10.7 Automatic Generation of Aesthetic Calligraphy 276
10.8 Intelligent Calligraphy Tutoring System 278
10.9 Conclusion and Future Work 280
10.9.1 Conclusion 280
10.9.2 Discussion and Future Work 281
References 285
Part V Animating Chinese Paintings
11 Animating Chinese Paintings through Stroke-based Decomposition 289
11.1 Overview 289
11.2 Introduction 289
11.3 Painting Decomposition Approach 292
11.3.1 Image Segmentation 295
11.3.2 Stroke Extraction by Region Merging 295
11.3.3 Stroke Refinement and Appearance Capture 301
11.3.4 Thin Brush Strokes 302
11.4 Appearance Capture and Synthesis of Single Brush Strokes 303
11.4.1 Single-stroke Appearance Model 303
11.4.2 Why Direct Texture Mapping is Inadequate 304
11.5 Separating Overlapping Brush Strokes 306
11.6 Decomposition and Reconstruction Results 309
11.7 Animating Paintings 312
11.8 Discussion 313
11.9 Conclusion and Future Work 319
11.9.1 Conclusion 319
11.9.2 Future Work 319
References 321
Part VI Perspectives
12 Final Fantasies for Digital Painting and Calligraphy 327
12.1 Perspectives on Digital Paintbrush Research 327
12.1.1 An Ideal Digital Paintbrush System 327
12.1.2 A Surreal Digital Paintbrush System 331
12.2 Perspectives on Intelligent Calligraphy Research 340
12.2.1 An Ideal Intelligent Calligraphy System 340
12.2.2 Intelligent Calligraphy System for Font Applications 342
12.2.3 Intelligent Calligraphy Study for Other Applications 345
12.3 An Ideal Painting Animation System 347
References 348
Index 353
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Add Computational Approach to Digital Chinese Painting and Calligraphy, A Computational Approach to Digital Chinese Painting and Calligraphy is a technical book on computer science and its applications in the arts. It focuses on Oriental digital arts, in particular Chinese arts and painting, offering a multi-disciplinary tr, Computational Approach to Digital Chinese Painting and Calligraphy to the inventory that you are selling on WonderClubX
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Add Computational Approach to Digital Chinese Painting and Calligraphy, A Computational Approach to Digital Chinese Painting and Calligraphy is a technical book on computer science and its applications in the arts. It focuses on Oriental digital arts, in particular Chinese arts and painting, offering a multi-disciplinary tr, Computational Approach to Digital Chinese Painting and Calligraphy to your collection on WonderClub |