The purpose of this volume is to present current work of the Intelligent Computer Graphics community, a community growing up year after year. This volume is a kind of continuation of the previously published Springer volume "Artificial Int- ligence Techniques for Computer Graphics". Nowadays, intelligent techniques are more and more used in Computer Graphics in order, not only to optimise the pr- essing time, but also to find more accurate solutions for a lot of Computer Gra- ics problems, than with traditional methods. What are intelligent techniques for Computer Graphics? Mainly, they are te- niques based on Artificial Intelligence. So, problem resolution (especially constraint satisfaction) techniques, as well as evolutionary techniques, are used in Declarative scene Modelling; heuristic search techniques, as well as strategy games techniques, are currently used in scene understanding and in virtual world exploration; multi-agent techniques and evolutionary algorithms are used in behavioural animation; and so on.
However, even if in most cases the used intelligent techniques are due to Artificial - telligence, sometimes, simple human intelligence can find interesting solutions in cases where traditional Computer Graphics techniques, even combined with Artificial Intelligence ones, cannot propose any satisfactory solution. A good example of such a case is the one of scene understanding, in the case where several parts of the scene are impossible to access.
Realistic Skin Rendering on GPU.- Affective States in Behavior Networks.- Information Theory Tools for Viewpoint Selection, Mesh Saliency and Geometry Simplification.- Classifying Volume Datasets Based on Intensities and Geometric Features.- Light Source Storage and Interpolation for Global Illumination: A Neural Solution.- An Intelligent System for Overlaying Texts on Background Images Based on Computational Aesthetics.- Parallel Coordinates: Intelligent Multidimensional Visualization.- An Adjectival Interface for Procedural Content Generation.- An SVM/GA Hybrid Framework for Qualitative Knowledge Aided 3D Scene Synthesis.- Machine Learning and Pattern Analysis Methods for Profiling in a Declarative Collaorative Framework.- AURAL: An Evolutionary Interface for a Robotic Sonification Process.
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