Numerical simulations of global warming, Mars observation data, and aircraft design are but a few of the topics where the use of human visual perception for data understanding are considered essential. Ten years agoa handful of pioneers professed the value of visualization to skeptical audiences. Today, with supercomputers and sensors producing ever-increasing amounts of data, scientific visualization is accepted throughout much of science and engineering as the fundamental tool for data analysis. Written by a world-wide panel of visualization experts, Scientific Visualization: Advances and Challenges presents astute coverage of prevailing trends, issues, and practice of scientific visualization. From algorithmic topics such as volume graphics and the modeling and visualization of large data sets, to foundations, perception, and interface technology (including virtual reality), this book provides the latest advances in the area. The book demonstrates new techniques, examines diverse application areas, and discusses current limitations and upcoming requirements. Scientific Visualization:Advances and Challenges $> presents readers with a unique opportunity to examine expert thinking and current practice, and to obtain a vision of potential future directions. It will be essential reading for scientific and engineering practitioners and visualization researchers alike.

Table of Contents:
Volume Visualization: A. Kaufman,<$> Trends in Volume Visualization and Volume Graphics. K.H. Hihne, A. Pommort, M. Riemer, T. Schiemann, R. Schubert, and U. Tiede,<$> Medical Volume Visualization Based on Intelligent Volumes. W. Kruger and P. Schroder,<$> Data Parallel Volume Rendering. Interface Technology and Perception:<$> J. Encarnacao and M. Fruhauf,<$> Global Information Visualization--The Visualization Challenge for the 21st Century. S. Bryson,<$> Real-Time Exploratory Scientific Visualization and Virtual Reality. C. Beshers and S. Feiner,<$> Automated Design of Data Visualizations. J. Foley and B. Ribarsky,<$> Next-generation Data Visualization Tools. N. Gershon,<$> From Perception to Visualization. Visualizing Large Data Sets:<$> G. Nielson,<$> Research Issues in Modeling for the Analysis and Visualization of Large Data Sets. P. Brunet, R. Juan, I. Navazo, A. Puig, J. Sole, and D. Tost,<$> Modeling and Visualization Through Data Compression. M. Gross,<$> Subspace Methods for the Visualization of Multidimensional Data Sets. H. Hagen,<$> Visualization of Large Data Sets. Foundations and Systems:<$> N.M. Thalmann and D. Thalmann,<$> Computer Animation: A Key Issue forTime Visualization. R.A. Earnshaw and M. Jern,<$> Fundamental Approaches to Interactive Real-Time Visualization Systems. J. Gallop,<$> Underlying Data Models and Structures for Visualization. S. Causse, F. Juaneda, and M. Grave,<$> Partitioned Objects Sharing for Visualization in Distributed Environments. T. Fruhauf, M. Gibel, H. Haase, and K. Karlsson,<$> Design of a Flexible Monolithic Visualization System. P. Robertson and L. De Ferrari,<$> Systematic Approaches to Visualization: Is a Reference Model Needed? Modeling Complexity:<$> Y. Shinagawa, T. Kunii, A. Fomenko, and S. Takahashi,<$> Coding of Object Surfaces Using Atoms. M. Novak,<$> Fractal Geometry and Its Applications in Visualization. J. Rossignac,<$> Representing and Visualizing Complex Continuous Geometric Models. A. Gagalowicz,<$> Modeling Complex Indoor Scenes Using an Analysis/Synthesis Framework. Applications:<$> F. Post and J. van Wijk,<$> Visual Representation of Vector Fields: Recent Developments and Research Directions. D.I. Abramov, V.V. Gusev, S.V. Klimenko, W. Kruger, L.I. Ponomarev, and W. Renz,<$> Visualization of the Quantum Coulomb Three-Body Problem in the Adiabatic Hyperspherical Approach. E. De Jong,<$> Solar System Visualization:Global Science Maps. L. Hesselink and T. Delmarcelle,<$> Visualization of Vector and Tensor Data Sets. L.J. Rosenblum and B. Kamgar-Parsi,<$> Progress and Problems in Ocean Visualization. L.J. Rosenblum,<$> Research Issues in Scientific Visualization.<$> Appendix. Subject Index.