Scientific Visualization

Motivation

Reflecting the heterogeous structure of Scientific Visualization, we want to concentrate on:

• Knowledge assisted visualization
  Knowledge assisted visualization is an emerging area of visualization research that addresses the development of advanced techniques that incorporate domain knowledge, statistical knowledge, perception and cognition knowledge, machine learning and artificial intelligence, as well as other knowledge from other disciplines to aid scientific researchers in finding relevant information from their massive datasets. Applications that can benefit from improved feature/signature representation and visualization include most scientific disciplines, ranging from atmospheric science to medicine to mechanical engineering.
• Visual Exploration Environment
  As the complexity, variety, and size of data generated from experiments, observatories, and simulations increases, visual representations are needed throughout the entire scientific process, from data collection to model fitting, to data reduction and extraction, to analysis, and finally to decision making. To manage this scope and complexity requires the development of interactive visual exploration environments where scientists and knowledge managers can visually interact and explore different portions of their data, different data sources, and interactively visually reason and make decisions. Creating these environments requires research in interaction science, integrated visual analysis, interface design, and advanced visualization techniques.
• Bio Med Vis
  Biomedical visualization and imaging refers to the mechanisms and techniques utilized to create and display images of the human body, organs or their components for clinical or research purposes. Typically these techniques attempt to reveal internal aspects of the body or unknown structures for diagnosis or therapeutics, using medical (radiological) scanners, endoscopy, laparoscopy, microscopy, biomedical thermography, biomedical photography, etc. Since biomedical visualization and imaging noninvasively produce the images of inner structures, it is gaining strong momentum, has already created life-saving solutions, and has the potential to revolutionize health care. Computational and algorithmic biomedical imaging is a wide area of research and solution development
• Visualization of Vector- and Tensorfields
  Vector and tensor field visualization means analysis, graphical representation and interactive visual access to simulation and measurement data of vector or tensor type. Such data stems mainly from engineering disciplines like automotive, aerospace, civil, electrical or mechanical engineering and exact sciences like biochemistry, chemistry or physics. Besides ever growing simulations, these disciplines challenge the visualization community with a strong increase of measured vector and tensor field data from particle image velocimetry (PIV) in fluid mechanics, nuclear magnetic resonance (NMR) in material sciences, diffusion tensor imaging (DTI) and perfusion measurements in medicine providing new visualization challenges. Current fundamental research trends aim at an integrated view on unsteady multi field simulations or measurements, visualization of higher order tensor fields, a deeper structural visual analysis based on physical or mathematical principles, and an effective incorporation of uncertainty in visualizations.