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CS591: Introduction

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.

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CS591: Introduction

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  1. CS591: Introduction Mengxia Zhu Fall 2007

  2. 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.

  3. 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

  4. My expectation • Experience in C programming • Basic Algebra and calculus • Basic understanding of computer graphics and OpenGL • A little deprivation of sleep…

  5. 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

  6. Office Hours • Regular Hours • M, W, F: 12:00PM — 12:50PM • Special Hours • Any time by appointment • Contact Info • Office: Faner 2142 • Email: mzhu@cs.siu.edu • Phone: (618)453-6057

  7. Computer Graphics for Visualization • OpenGL • Drawing geometric objects • Viewing • Interception and Culling • Lighting and Shading • Special topics

  8. Scientific Visualization • Isosurface rendering • Volume rendering • Splatting • Raycasting • Vector and tensor visualization

  9. 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

  10. Visualization Process Measured/Scanned Data: -CT, MRI, ultrasound Computation: -simulation/modeling Financial data: -transactions per day Data Transform Map Display

  11. 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

  12. Relation To Other Fields IlluminationEngineeringOptics Signal/ImageProcessing Vision ComputationalGeometry Visualization AppliedMathematics PsychologyCognition UserInterfaces Hardware

  13. 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)

  14. Human Inner Organs • Visible (voxel) man • Reconstruction of human body from tomographic datasets of dissected real body www.uke.uni-hamburg.de

  15. Stars and Emission Nebulas • Visualizing Orion Nebula: • Nadeau et al., Computer • Graphs Forum, 20: 27 • (2001)

  16. Crystal Structure • MgSiO3 perovskite • An orthorhombic unit cell • Atomic coordination

  17. 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

  18. 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

  19. 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

  20. Example: Air Flow over Windshield • Air flow coming from a dashboard vent and striking the windshield of an automobile • http://www-fp.mcs.anl.gov/fl

  21. Measurement: Medical Imaging Volume rendered brain image Standard brain CT image Ultrasound http://www.gemedicalsystems.com

  22. Challenges? • Scale • Dimensionality • Data types • Presentation • Interactivity

  23. 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

  24. 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

  25. 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

  26. 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

  27. Interactivity • Visualization is naturally interactive • Real-time interactions, i.e, virtual environments • Show multiple different perspectives on the data

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