Lecture overview. Visualisatie BMT. Goal. Summary (1) Summary (3) Summary (2) Goal Summary Study material

Transcription

1 Visualisatie BMT Introduction, visualization, visualization pipeline Arjan Kok Lecture overview Goal Summary Study material What is visualization Examples Visualization pipeline 1 2 Goal Provide theoretical and practical knowledge in: Data visualization Data representation Computer graphics Data processing in Java Visualization in MayaVi Summary (1) Introduction What is visualization Related disciplines Fields of applications The visualization pipeline Definition Data enrichment, mapping, rendering 3 4 Summary (2) Basic data representation Datasets Sampling Interpolation Graphics rendering Rendering process Color Lighting, shading Summary (3) Algorithms Scalar algorithms Vector algorithms Tensor algorithms Modeling algorithms Volume visualization Ray tracing, ray sampling Volume interpolation 5 6 1

2 Study material Theory Book Slides Practice MayaVi (visualization tool) RealJ (java programming tool) Assignments Book The Visualization Toolkit An Object-Oriented Approach to 3D Graphics W. Schroeder, K. Martin, B. Lorensen Prentice Hall Book contains a lot more than the course does (course will address specific parts/chapters) Book contains software we shall not (directly) use 7 8 Slides Visualization Slides used in lectures will be available at: What do we visualize? Visualization The purpose of computing is insight, not numbers - Richard Hamming

3 Visualization - insight in data From data to pictures Attributes of Visualization Making abstract data visible (complex, many) Forming a mental image of something abstract Using the abilities of human vision and interaction E a: b: 43.3 C: Scientific visualization The use of computer imaging techniques as a too for comprehending data obtained by simulation or physical measurements The techniques that allow scientists and engineers to extract knowledge from the results of simulations and computations Goals in visualization Exploration of data and information Enhancing understanding of concepts and processes Gaining new (unexpected) insight Making invisible visible Effective presentation of significant features Quality control of simulations and measurements Increasing scientific production Visualization challenges Getting usable data Parsable Visualizable Defining your goal What is the focus of attention or primary features Who is the audience What is the message Choosing meaningful/compelling visual representations Graphs

4 Complex data Some examples We are interested in more complex data Multi-dimensional Complex geometry Computed or collected Simulations MRI, CAT,.. Microscopic to galactic data collections Related disciplines IMAGE PROCESSING PERCEPTUAL PSYCHOLOGY COMPUTER AIDED DESIGN USER INTERFACE STUDIES V I S U A L I Z A T I O N GEOMETRIC MODELING COMPUTER GRAPHICS SIGNAL PROCESSING Imaging, graphics, visualization Imaging The study of 2D images (transformations, enhancement, information extraction) Graphics Creating images using a computer (2D drawing techniques, 3D rendering techniques) Visualization Exploring, transforming, and viewing data as images Imaging, graphics, visualization Imaging, graphics, visualization Visualization uses computer graphics and imaging as tools for the higher level goal of getting insight into data Imaging Graphics Visualization Graphics and imaging are particular forms of visualization Data dimensionality 2D 2D, 3D nd Data transformation image image 2D/3D object image any data image

5 Applications Applications Biochemistry Molecular modeling/dynamics Industrial research on molecular structures Drug design molecule structures Molecular visualization Molecular visualization Applications Mathematics Understanding complex concepts (functions, surfaces, fields,..) Mathematics z = F(x,y) = e -r cos(10r) functions f(x,y,z) nested implicit functions function plot 29 saddle quadric surface F(x,y,z) =

6 Applications Medicine Medicine Diagnosis Treatment planning Education Research 2D/3D scan data surfaces/ slices Examples Geosciences Weather forecast Topography Geology Geosciences Rain during summer 2004 surface/ volume data surfaces/ height plots 33 Ocean surface height during the El Nino event 34 Applications Space sciences Space sciences Astronomy Astrophysics Remote sensing Orion Nebula as seen from a virtual spacecraft

7 Applications Computational fluid dynamics Engineering and physics Computational fluid dynamics Fluid flow simulation Surface modeling Finite element simulations Physical processes (strength, elasticity, flow,..) air pressure on a plane wing velocity of a turbulent jet flow 37 internal waves inside the ocean 38 Finite element methods pressure on a plane wing Applications Architecture Simulations of: Indoor lighting Sound Heath Air 2D flow past a cylinder Architecture Simulation of light in a theatre Applications Visualization is applicable in any research or engineering field E a: b: 43.3 C:

8 Visualization pipeline Describes the steps to transform raw data into displayable images Goal of these steps is to convert the information to a format amenable to understanding by the human perceptual system while maintaining the integrity of information Visualization pipeline Raw Data Data Enrichment/Enhancement Derived Data Visualization Mapping Abstract Visualization Object Rendering 43 Displayable Image 44 Getting the data Step 1: Data enrichment Simulation data Data formats HDF, NetCDF, XDR, Dicom,. Data compression Measured data RLE, Fractal methods,. my own format Data enrichment Interpolation Filtering and smoothing Selection Merging Format conversion 2D and 3D conversions (rotation, translation) Visualization internal data (ready for the pipeline) data object(s) data enrichment (filter object) data object(s) Step 2: Mapping Mapping Generating displayable data (2D and 3D objects) whose shape, dimensions and color represent the enriched data Abstract visualization objects The 2D and 3D objects resulting from the mapping stage (graphical primitives) Step 3: Rendering Rendering Produces an image (view) of the 2D/3D abstract visualization objects Several rendering parameters (lighting, shadows, reflections, etc) data object(s) mapping (mapper object) abstract visualization objects abstract visualization objects rendering image(s)

9 Step 3: Rendering Example Rendering Special rendering techniques such as volume rendering for non-opaque data data object(s) volume rendering image(s) Example pipeline reader Visualization and interaction Raw Data data str. pnts outline filter geometry filter data polydata data polydata mapper mapper mapper lines surfaces surface render image u s e r i n p u t Data Enrichment/Enhancement Derived Data Visualization Mapping Abstract Visualization Object Rendering Displayable Image Visualization and research process Visualization and research process Visualization plays a large role in forming the link between hypothesis and experiment, and between insight and new hypothesis

10 Visualization pipeline (revisited) Raw Data Data Enrichment/Enhancement Derived Data Visualization Mapping Abstract Visualization Object Rendering Displayable Image 55 10