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CS591: Introduction Mengxia Zhu Fall 2007 Class objective To study visualization principles, techniques and algorithms which are used for exploring, transforming and viewing data as computer images to gain understanding and insight into the data. Introduction to basics of parallel computing and MPI for large scale scientific datasets. Course materials No textbook required Lecture notes Posted on http://www.cs.siu.edu/~mengxia/Teaching.htm Research papers Distributed/referred in class Web sources Referenced in lectures My expectation Experience in C programming Basic Algebra and calculus Basic understanding of computer graphics and OpenGL A little deprivation of sleep… Grading Policy Midterm and final exam Grading items: Homework: 20% Mid term and final exam: 30% Lab projects: 40% Paper presentation: 10% Grading Scale: A = 85% or more B = 75% to 84% C = 65% to 74% D = 50% to 64% F = below 50% Late submission will be punished. Academic dishonesty will be treated seriously Office Hours Regular Hours M, W, F: 12:00PM — 12:50PM Special Hours Any time by appointment Contact Info Office: Faner 2142 Email: [email protected] Phone: (618)453-6057 Computer Graphics for Visualization OpenGL Drawing geometric objects Viewing Interception and Culling Lighting and Shading Scientific Visualization Isosurface rendering Volume rendering Splatting Raycasting Vector and tensor visualization What Visualization? Process of making a computer image or graph for giving an insight on data/information Transforming abstract, physical data/information to a form that can be seen Interpreting in visual terms or putting into visual forms (i.e., into pictures) Cognitive process Form a mental image of something Internalize an understanding Visualization Process Computation: Measured/Scanned Data: -CT, MRI, ultrasound Financial data: -transactions per day -simulation/modeling Data Transform Map Display Viz vs. Graphics vs.. Imaging Imaging - Enhance, analyze, manipulate images Graphics - Make pictures! geometric data is stored in the computer for the purposes of performing calculations and rendering 2D images Visualization - Exploration, transformation, viewing data as images Relation To Other Fields Illumination Signal/Image Engineering Processing Optics Computational Vision Geometry Visualization Applied Psychology Mathematics Cognition User Hardware Interfaces Why? Extends our vision Removes limits of human vision in space, time, frequency and complexity Creates images or pictures of things that otherwise can not be seen See an object’s internal structure (visible man) See things that are far away or slow in evolution (stars and nebulas) See microscopic world (crystal structure) See things that move very fast (molecular dynamics) Human Inner Organs Visible (voxel) man Reconstruction of human body from tomographic datasets of dissected real body www.uke.uni-hamburg.de Stars and Emission Nebulas Visualizing Orion Nebula: Nadeau et al., Computer Graphs Forum, 20: 27 (2001) Crystal Structure MgSiO3 perovskite An orthorhombic unit cell Atomic coordination Types of Visualization Scientific Visualization Scientific data Information Visualization abstract data has no inherent spatial structure thus it does not allow for a straightforward mapping to any geometry with arbitrary relationship Data Visualization A more general term data sources beyond science such as financial, marketing, or business data Broad enough to encompass both scientific and information visualization Scientific Visualization Relates to and represents something physical or geometric Images of human brain Air flow over a wing Data come from scientific computing and measurements Scientific Computing Real materials simulation/modeling Electronic calculations Atomistic MD (molecular dynamics) modeling Finite element (continuum) modeling Solving differential equations Computational fluid dynamics Temperature distribution Electromagnetic field Example: Air Flow over Windshield Air flow coming from a dashboard vent and striking the windshield of an automobile http://wwwfp.mcs.anl.gov/ fl Measurement: Medical Imaging Standard brain CT image Volume rendered brain image Ultrasound http://www.gemedicalsystems.com Challenges? Scale Dimensionality Data types Presentation Interactivity Data Explosion How to make sense out of the datasets when they become very large Scientific data A million-atom simulation: 7 GB/step Satellite or space station: TB/day MRI dataset: 2563 = 16 MB/slice Laser scanning: 2 million points/minute Dimensionality Three dimension (trivariate data) We are in 3D world Volume visualization (mapping 3D data to 2D screen) Multidimension (hypervariate data) Car attributes: Make, model, year, miles per gallon, cost, no. of cylinders, size, weight How to display relationships between many variables Data Types Structured versus unstructured data Unstructured (irregular) data are less compact and efficient Preprocessing of data Scalar, vector and tensor data Density, temperature Data from flow dynamics Stress-strain data Non-numerical data Ordinal: days of the week Categorical data: names of animals Presentation Problem Display without ambiguity Colors, lighting, translucent, animation, texture mapping Too much data for too little display area (screen) Too many cases Too many variables Need to highlight particular cases or variables Interactivity Visualization is naturally interactive Real-time interactions, i.e, virtual environments Show data multiple different perspectives on the