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Intelligent Scene Modelling Information Systems 2009 ed. [Hardback]

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  • Formāts: Hardback, 216 pages, height x width: 235x155 mm, weight: 550 g, 10 Illustrations, color; 103 Illustrations, black and white; XII, 216 p. 113 illus., 10 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 181
  • Izdošanas datums: 09-Mar-2009
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540929010
  • ISBN-13: 9783540929017
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Intelligent Scene Modelling Information Systems 2009 ed.Palielināt
  • Formāts: Hardback, 216 pages, height x width: 235x155 mm, weight: 550 g, 10 Illustrations, color; 103 Illustrations, black and white; XII, 216 p. 113 illus., 10 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 181
  • Izdošanas datums: 09-Mar-2009
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540929010
  • ISBN-13: 9783540929017
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783540929017.html
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This book is dedicated to and contains the latest research in intelligent scene modelling information systems. Declarative scene modeling techniques are presented, as well as their implementation in an intelligent information system.



Scene modeling is a very important part in Computer Graphics because it allows c- ating more or less complex models to be rendered, coming from the real world or from the designer’s imagination. However, scene modeling is a very difficult task, as there is a need of more and more complex scenes and traditional geometric modelers are not well adapted to computer aided design. Even if traditional scene modelers offer very interesting tools to facilitate the designer’s work, they suffer from a very important drawback, the lack of flexibility, which does not authorize the designer to use incomplete or imprecise descriptions, in order to express his (her) mental image of the scene to be designed. Thus, with most of the current geometric modelers the user must have a quite precise idea of the scene to design before using the modeler to achieve the modeling task. This kind of design is not really a computer aided one, because the main creative ideas have been elaborated without any help of the modeler. Declarative scene modeling could be an interesting alternative to traditional g- metric modeling. Indeed, declarative scene modeling tries to give intuitive solutions to the scene modeling problem by using Artificial Intelligence techniques which allow the user to describe high level properties of a scene and the modeler to give all the solutions corresponding to imprecise properties.
1 Intelligent Scene Modelling Information Systems: The Case of Declarative Design Support 1
Georgios Miaoulis
1.1 Introduction
1
1.2 The Scene Modelling Process in Declarative Design Support
3
1.3 Information, Knowledge and Scene Models Representations
9
1.3.1 Physical Scene Models
12
1.3.2 Conceptual Scene Models – Generic Models
14
1.3.3 Scene Conceptual Modelling in MultiCAD
16
1.4 Software Architectures for Declarative Design Support
21
1.4.1 MultiCAD: Objectives, Constraints and Functional Choices
22
1.4.2 Definition of MultiCAD Framework-Architecture
23
1.5 Conclusion
25
References
26
2 Declarative Modeling in Computer Graphics 29
Dimitri Plemenos
2.1 Introduction
29
2.2 What Is Declarative Scene Modeling
30
2.3 Imprecision Management in Declarative- Modelers
31
2.4 A Classification of Declarative Scene Modelers
32
2.4.1 Modelers Using Exploration Mode in Scene Generation
32
2.4.2 Modelers Using Solution Search Mode in Scene Generation
35
2.4.3 Other Declarative or Declarative-Like Modelers
38
2.5 Scene Understanding in Declarative Scene Modeling
40
2.6 Constraint Satisfaction Techniques for Declarative Scene Modeing
41
2.6.1 Arithmetic Constraint Satisfaction Techniques
41
2.6.1.1 The Resolution Process
41
2.6.1.2 Constraint Logic Programming on Finite Domains – CLP( FD)
42
2.6.1.3 Hierarchical Decomposition-Based Improvements
43
2.6.2 Geometric Constraint Satisfaction Techniques
44
2.6.2.1 Principles of the MultiFormes Geometric Constraint Solver
45
2.6.2.2 The Resolution Process
45
2.6.2.3 The Intersection and Sampling Problems
45
2.6.2.4 Some Other Problems
46
2.6.3 Discussion
47
2.6.3.1 Arithmetic CSP
47
2.6.3.2 Geometric CSP
48
2.7 Declarative Scene Modeling and Machine-Learning Techniques
48
2.7.1 A Dynamical Neural Network for Filtering Unsatisfactory Solutions in DMHD
49
2.7.1.1 Structure of the Used Network
49
2.7.1.2 The Machine Learning Process
51
2.7.1.3 Discussion
52
2.8 Advantages and Drawbacks of Declarative Scene Modeling
53
2.9 Future Issues
54
2.10 Conclusion
55
References
55
3 Understanding Scenes 59
Vassilios S. Golfinopoulos
3.1 Introduction to Reverse Engineering
59
3.1.1 Reverse Engineering in Scene Modelling
60
3.1.2 Reverse Engineering and Geometric Modelling
62
3.1.3 Reverse Engineering and Feature-Based Modelling
63
3.1.4 Reverse Engineering and Declarative Modelling
65
3.2 Integration of the Two Models
68
3.3 Reconstruction Phase
69
3.4 Extended Design Methodology
70
3.5 System Architecture
71
3.5.1 Data and Knowledge Storage
73
3.5.2 The Stratified Representation
74
3.5.3 Extraction of Relations and Properties
77
3.5.4 Scene Modifications
78
3.5.5 The Propagation Policy
79
3.5.6 The Unified Stratified Representation
81
3.5.7 The Resultant Declarative Description
82
3.6 Conclusions
83
References
85
4 Intelligent Personalization in a Scene Modeling Environment 89
Georgios Bardis
4.1 Introduction
89
4.2 Intelligent Personalization and Contributing Fields
90
4.3 Preference Model
92
4.3.1 Preference Structure
92
4.3.2 User Preference as a Function
94
4.4 Multicriteria Decision Support
95
4.4.1 Outranking Methods
96
4.4.2 Weighted Sum Methodologies
97
4.5 Machine Learning
97
4.5.1 Traditional Machine Learning Mechanisms
98
4.5.2 Incremental Learning
99
4 5.3 Imbalanced Datasets
100
4.5.4 Context Specific Issues
101
4.6 Intelligent Personalization in a Scene Modeling Environment
102
4.6.1 Scene Representations
102
4.6.2 Scene Modeling Process
104
4.6.2.1 Solution Generation: Constraint Satisfaction Techniques
104
4.6.2.2 Solution Generation: Evolutionary Techniques
105
4.6.2.3 Solution Visualization
105
4.6.2.4 Scene Modeling Environment
106
4.6.3 Preferences Acquisition
106
4.6.3.1 Solution Encoding for Preferences Acquisition
107
4.6.3.2 Preferences Acquisition via Incremental Learning
107
4.6.3.3 User-Assisted Acquisition of Preferences
108
4.7 Intelligent User Profile Module Architecture
110
4.7.1 Declarative Modeling
110
4.7.2 Module Architecture
112
4.8 Experimental Results
114
4.8.1 Performance Indices and Representative Scenes
114
4.8.2 Experiment Series
115
4.9 Conclusion
117
References
117
5 Web-Based Collaborative System for Scene Modelling 121
John Dragonas, Nikolaos Doulamis
5.1 Introduction
121
5.1.1 Research Scope
123
5.2 Related Work
124
5.2.1 Collaborative Design
124
5.2.1.1 Collaborative Systems
124
5.2.2 Declarative Design
126
5.2.3 Overview of MultiCAD Architecture
127
5.2.4 DKABM Framework
128
5.2.5 Declarative Design Representations
128
5.2.6 Collaborative Declarative Modelling System
129
5.3 Web-Based CDMS Framework
129
5.3.1 Declarative Collaborative Module
130
5.4 Case Study
137
5.4.1 Study of Collaborative Activity
137
5.5 Team Profile Module
138
5.5.1 Single Designer Approach
138
5.5.1.1 Intelligent Profile Estimation
139
5.5.1.2 Recursive Implementation
140
5.5.2 Collaborative Approach
142
5.5.2.1 Preference Consensus Module
142
5.5.2.2 Collaborative Clustering
144
5.5.3 Simulations
146
5.6 Conclusions
147
References
148
6 Aesthetic – Aided Intelligent 3D Scene Synthesis 153
Dimitrios Makris
6.1 Introduction
153
6.1.1 Research Scope
154
6.1.2 Proposed Methodology – Contributing Areas
154
6.2 Related Work
155
6.2.1 Evolutionary Computing Techniques
156
6.2.1.1 Evolutionary Design
157
6.2.1.2 Genetic Algorithm Applications in Design
157
6.2.2 Computational Aesthetic Approaches
159
6.2.3 Style Modelling Approaches
160
6.2.3.1 The Concept of Style
160
6.2.4 MultiCAD Framework Style
162
6.3 Research Approach
162
6.3.1 Architectural Style Modelling
163
6.3.1.1 Style Knowledge Framework
163
6.3.1.2 Measure of Style
169
6.3.2 Multi-objective Genetic Algorithm
169
6.3.2.1 Genetic Algorithm
170
6.3.2.2 MOGA Mechanism
171
6.4 Implementation Framework
172
6.4.1 Software Architecture
172
6.4.1.1 User Interface Layer
173
6.4.1.2 Processing Layer
173
6.4.1.3 Data Management Layer
174
6.5 System Evaluation
174
6.6 Discussion
178
6.7 Conclusions
179
6.7.1 Declarative Modelling and Architectural Conceptual Design
179
6.7.2 Aesthetic and Artificial Intelligence
180
References
180
7 Network Security Surveillance Aid Using Intelligent Visualization for Knowledge Extraction and Decision Making 185
Ioannis Xydas
7.1 Introduction
185
7.1.1 Web Security
186
7.1.2 Intrusion Detection
186
7.1.3 Visualization
187
7.1.4 Visual Data Analysis
188
7.1.5 Research Objectives
189
7.2 Related Work
191
7.3 Visualization Prototype System
192
7.3.1 Data Capture Module
193
7.3.2 Pre-processor Module
194
7.3.3 Knowledge Base Module
194
7.3.3.1 Classes of Web Attacks
194
7.3.3.2 Training Data Quality
196
7.3.3.3 Evolutionary Artificial Neural Network
196
7.3.3.4 EANN Performance Versus ANN
199
7.3.4 Graph Generator Module
200
7.3.5 Statistical Analysis Module
205
7.4 Prototype System Performance
206
7.4.1 Introduction
206
7.4.2 Classification
206
7.4.2.1 Neyman-Pearson Decision Rule
206
7.4.2.2 Sufficient Statistics and Monotonic Transformations
207
7.4.2.3 Neyman-Pearson Lemma: General Case
208
7.4.3 Detection, False and Miss Probabilities of the Prototype System
208
7.4.4 ROC Curve of the Prototype System
210
7.5 Conclusion
212
References
213
Author Index 215