Mathematical Progress in Expressive Image Synthesis I: Extended and Selected Results from the Symposium MEIS2013 [Hardcover]

This book presents revised versions of the best papers selected from the symposium Mathematical Progress in Expressive Image Synthesis (MEIS2013) held in Fukuoka, Japan, in 2013. The topics cover various areas of computer graphics (CG), such as surface deformation/editing, character animation, visual simulation of fluids, texture and sound synthesis and photorealistic rendering. From a mathematical point of view, the book also presents papers addressing discrete differential geometry, Lie theory, computational fluid dynamics, function interpolation and learning theory. This book showcases the latest joint efforts between mathematicians, CG researchers and practitioners exploring important issues in graphics and visual perception.

The book provides a valuable resource for all computer graphics researchers seeking open problem areas, especially those now entering the field who have not yet selected a research direction.Part I?Mathematical approaches to computer graphics and vision.-?The Power of Orthogonal Duals.- Mathematical Models of Visual Information Processing in the Human Brain and Applications to Visual Illusions and Image Processing.- Decomposition and clustering for the visualization of dynamical system.- Probable and Improbable Faces.- Part II Sound and scene rendering.- Progress in Digital Sound Synthesis for Physically Based Animation.- Efficient Image-based Rendering Method using Spherical Gaussian.- A Lie theoretic proposal on algorithms for spherical harmonic lighting.- Interactive Editing of Volumetric Objects by Using Feature-based Transfer Function.- Feature-based Approach for the Interactive Editing of Environmental Lighting Effects.- Ray Tracing of Quadratic Parametric Surface.- Part III Fluid and flow.- A Flexible Image Processing Approach to the Surfacing of Particle-Based Fluid Animation.- Inverse Approach for Visual Simulation of Clouds.- Generating Flow Fields Variations using Laplacian Eigenfunctions.- Blood flow analysislc