CSE 694L- Visualization Raghu Machiraju Dreese Laboraories, 779 [email protected] www.cse.ohio-state.edu/~raghu Outline Introduction Visualization pipeline Data acquisition and data structures Basic visual mapping approaches Scalar fields (isosurfaces + volume rendering) Vector and field visualization Perception + Interaction Issues Graphs/Trees + HighD Data 22 Syllabus 33 Sources Selective contributions from - Hanspeter Pfister, Harvard University - Torsten Moeller, Simon Fraser U, Canada - Tamara Munzner, University of British Columbia - Melanie Tory, U of Victoria, Canada - Daniel Weiskopf, TU Stuttgart, Germany 44 1. Introduction What is visualization? Definitions and goals 55 1.1. Definitions and Goals Oxford English Dictionary: to visualize: form a mental vision, image, or picture of (something not visible or present to sight, or of an abstraction); to make visible to the mind or imagination. Picture - Color, texture, patterns, objects - Spatial resolution, stereo, temporal resolution Here: visualization in scientific and technical environments - Not in education, marketing, …. 6 1.1. Definitions and Goals B. McCormick, T. DeFanti, and M. Brown: Visualization is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. It enriches the process of scientific discovery and fosters profound and unexpected insights. In many fields it is already revolutionizing the way scientists do science. McCormick, B.H., T.A. DeFanti, M.D. Brown, Visualization in Scientific Computing, Computer Graphics 21(6), November 1987 8 1.1. Definitions and Goals R. Friedhoff and T. Kiley: The standard argument to promote scientific visualization is that today's researchers must consume ever higher volumes of numbers that gush, as if from a fire hose, out of supercomputer simulations or high-powered scientific instruments. If researchers try to read the data, usually presented as vast numeric matrices, they will take in the information at snail's pace. If the information is rendered graphically, however, they can assimilate it at a much faster rate. R.M. Friedhoff and T. Kiely, The Eye of the Beholder, Computer Graphics World 13(8), pp. 46-, August 1990 9 1.1. Definitions and Goals R.B. Haber and D. A. McNabb: The use of computer imaging technology as a tool for comprehending data obtained by simulation or physical measurement by integration of older technologies, including computer graphics, image processing, computer vision, computer- aided design, geometric modeling, approximation theory, perceptual psychology, and user interface studies. R.B. Haber and D. A. McNabb, Visualization Idioms: A Conceptual Model for Scientific Visualization Systems, in Visualization in Scientific Computing, IEEE Computer Society Press 1990. 10 1.1. Definitions and Goals R.A. Earnshaw: Scientific Visualization is concerned with exploring data and information in such a way as to gain understanding and insight into the data. The goal of scientific visualization is to promote a deeper level of understanding of the data under investigation and to foster new insight into the underlying processes, relying on the humans' powerful ability to visualize. In a number of instances, the tools and techniques of visualization have been used to analyze and display large volumes of, often time-varying, multidimensional data in such a way as to allow the user to extract significant features and results quickly and easily. K.W. Brodlie, L.A. Carpenter, R.A. Earnshaw, J.R. Gallop, R.J. Hubbard, A.M. Mumford, C.D. Osland, P. Quarendon, Scientific Visualization, Techniques and Applications, Springer-Verlag, 1992. 1010 1.1. Definitions and Goals J. Foley and B. Ribarsky: A useful definition of visualization might be the binding (or mapping) of data to representations that can be perceived. The types of bindings could be visual, auditory, tactile, etc., or a combination of these. J. Foley and B. Ribarsky, Next-generation Data Visualization Tools, in Scientific Visualization, 1994, Advances and Challenges, Academic Press. 1111 1.1. Definitions and Goals H. Senay and E. Ignatius Scientific data visualization supports scientists and relations, prove or disprove hypotheses, and discover new phenomena using graphical techniques. The primary objective in data visualization is to gain insight into an information space by mapping data onto graphical primitives. H. Senay and E. Ignatius, A Knowledge-Based System for Visualization Design, IEEE Computer Graphics and Applications, pp. 36-47, November 1994. 1212 1.1. Definitions and Goals Insight and analysis - Extract the information content - Make things/coherences visible that are not apparent - Analyze the data by means of the visual representation Communication - Allow the non–expert to understand • Present specific information in a way that all of us understand - Guide the expert into the right direction 1313 • Steering (Exploration) - Interactively control and drive your application 1.1. Definitions and Goals Educational goals Visualization specialist Theory - Classification - Algorithms - Visual design Application - Methods - Visualization packages • Experience - How to visualize something in the best way 1414 1.2. History Techniques for finding visual representations for abstract data are not new! 1515 1.2. History Map of Catal Hyük 6200 BC Excavation Reconstruction 1616 1.2. History Map on clay of Ga-Sur 2500 BC • Map on clay • Reconstruction 1717 1.2. History Sung Dynasty, China (1137 BC) 1818 1.2. History Ptolemaic map of the world by Johan Scotus (1505 AD) 20 1.2. History Time series: inclination of planets (10th century) 2020 1.2. History Map of tracks of Yü the Great (11th century) 2121 1.2. History Vector visualization (1686) Height field (1879) Streamlines, arrow plots by Halley Census data by Perozzo 1.2. History Napoleon‘s campaign against Russia (1812/13) Cartography (1861) 1.2. History 1987 NSF Advisory Panel on Graphics and Image Processing - Computer experiments allow access to new worlds - Real experiments are too expensive, too dangerous, etc. - Arbitrary large and small time scales and spatial dimensions Direct implications - The data flood from supercomputer simulations can only be dealt with visually - This task needs a visualization specialist and an interdisciplinary team - New developments in hardware, software, nets, standards are necessary Advantages in the long term will be - Faster insight - Faster product–development cycles - Stronger position in global competition Suggestion: spend lots of money to support scientific visualization 1.3. Examples 2525 1.3. Examples Deutsches Klimarechenzentrum DKRZ Simulation of climate phenomena (volcanoes, atmosphere) 1.3. Examples Deutsches Klimarechenzentrum DKRZ Simulation of global warming. Worst case (left), with drastic political measures (right) 2727 1.3. Examples AVS Express Visualization of traffic in Dallas, Texas 1.3. Examples AVS Express Medical imaging: therapy planning 2929 1.3. Examples AVS Express Medical imaging: therapy evaluation 3030 1.3. Examples Varying the transfer function – the visible woman 1.3. Examples Aneurysms: vessel deformations in CTA 1.3. Examples 7T MRI Image 3D/4D Fetal Imaging CT Small Animal Imaging Coronary Artery Ulcerated Plaque Heart - CT Visualization - MRI 1.3. Examples CT scan 3434 1.3. Examples Blue: nuclei indicator Purple: neuron indicator Green: blood vessel wall indicator Red: astrocyte indicator (GFAP) Yellow: microglia indicator (Iba-1) 50 µm Prof. Badri Roysam, RPI 1.3. Examples Prof. Sean Megason, Harvard Medical School 800 Timesteps, Movie 1.3. Examples Jens Rittscher, GE Global Researcher More ZebraFish Body Parts - Eye 1.3. ExamplesDrs. Knopp, Sammett, Radiology, OSU Drs. Peter Basser, Carlo Pierpaoli, NIH DWI Tensors Fibers 1.3. Examples Dr. Morocz, Radiology, Harvard Medical 1.3. Examples AChE-FAS complex, showing that the snake venom blocks the entrance to the gorge. The active residues are shown as ball and stick. 1.3. Examples The secondary structure (alpha helices and beta sheets) of the protein Acetylcholinesterase. The aromatic residues (bonds) and the neurotransmitter Acetylcholine (molecular surface) lodged in the active site are also shown. [J. Sussman] 1.3. Examples Relation of secondary structure (alpha-helix) to primary structure formed by the polypeptide chain of amino acids. The helix is stabilized by Hydrogen bonding. 1.3. Examples Aromatic residues in active site (ball and stick) of AChE enzyme with molecular surface of ACh molecule. 1.3. Examples Active site in AChE showing Tachrine molecule used in the experimental drug Cognix to treat Alzheimer's disease. By inhibiting the enzyme's activity, Tachrine prevents the breakdown of the neurotransmitter ACh. 4444 1.3. Examples Visualization and Animation Laboratory at Center of Scientific Computing Finland Simulation of the docking of the CBH1 tail on a cellulose surface. 1.3. Examples Simulated events in the Relativistic Heavy Ion Collider Experiment PHENIX. Hits at the detector appear as yellow glyphs. Blue lines trace the probable tracks of products from the collision. [J. Mitchell] 1.3. Examples Generation of a ring of dust around a star 4747 1.3. Examples Simulation of a flow around a wheel (3D Line Integral Convolution) 1.3. Examples Flow visualization 1.3. Examples Flow visualization 5050 1.3. Examples Computed flow through a cerebral aneurysm. 5151 1.3. Examples Pseudo color image of the spray pattern generated