Scientific visualization

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Computers help us handle and process tons of information data. Most of the time all this data is so dense, it’s almost impossible to understand from just looking at a bunch of numbers. Some of the data could be analyzed by computers, but most of the time there must be somebody, a real thinking person, who shall interpret the data and take conclusions from it to make decisions, analyze. Scientific Visualization is about converting numbers into a representation of reality, something more graphic so that a human being can understand and/or communicate.

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Scientific visualization

  1. 1. SCIENTIFIC VISUALIZATION DOWN TO BASICS Emiliano Martínez Rivera Multimedia Creation, Design and Engineering Master 2011 BES La Salle, Universitat Ramon Llull - Barcelona, Spain ABSTRACT "A useful definition of visualization might be theComputers help us handle and process tons of information binding (or mapping) of data to a representation that can bedata. Most of the time all this data is so dense, it’s almost perceived. The types of binding could be visual, auditory,impossible to understand from just looking at a bunch of tactile, etc. or a combination of these." [5]numbers. Some of the data could be analyzed by computers,but most of the time there must be somebody, a real thinking "Visualization is more than a method of computing.person, who shall interpret the data and take conclusions Visualization is the process of transforming information intofrom it to make decisions, analyze. Scientific Visualization a visual form, enabling users to observe the information.is about converting numbers into a representation of reality, The resulting visual display enables the scientist or engineersomething more graphic so that a human being can to perceive visually features which are hidden in the data butunderstand and/or communicate. nevertheless are needed for data exploration and analysis." [6] Index Terms— Computer Science, Computer Graphics,Infographics, Visualization, Scientific Modeling, Shape, 1.2. Synthesis of DefinitionsArt, Animation, Engineering drawings, Virtual Reality,Image Processing [1] “Mapping from computer representations to perceptual (visual) representations, choosing encoding techniques to 1. INTRODUCTION maximize human understanding and communication” [2]The present paper aims to describe the Scientific 1.3. Scientific Visualization GoalsVisualization in clear terms so that any reader, no matter theknowledge level, can understand.  exploration/exploitation of data and information  enhancing understanding of concepts and processes1.1. Example Definitions [2]  gaining new (unexpected, profound) insights  making invisible visible "Visualization is a method of computing. It transforms  effective presentation of significant featuresthe symbolic into the geometric, enabling researchers to  quality control of simulations, measurementsobserve their simulations and computations. Visualization  increasing scientific productivityoffers a method for seeing the unseen. It enriches the  medium of communication/collaborationprocess of scientific discovery and fosters profound andunexpected insights. In many fields it is already 1.4. Adjacent Disciplinesrevolutionizing the way scientists do science." [3] Following is a general comparison of Visualization (V) with "Scientific visualization is a new, exciting field of some adjacent disciplines to better understand thecomputational science spurred on in large measure by the differences:rapid growth in computer technology, particular in graphicsworkstation hardware and computer graphics software.  Computer Graphics (CG)[Visualization tools] are beginning to impact our daily livesthrough usage in the arts, particularly film animation, and Efficiency of algorithms versus effectiveness of use.they hold great promise for scientific research andeducation. When computer graphics is applied to scientific  Computer Vision (CV)data for purposes of gaining insight, testing hypothesis, and Mapping from pictures to abstract description versusgeneral elucidation, we speak of scientific visualization." [4] mapping from abstract description to pictures.
  2. 2.  Image Processing (IP) => NSF Committee to solve problems Mapping from data domain to data domain versus mapping from data domain to picture domain Committee on "Graphics, Image Processing, and Workstations" (1986) (Visual) Perception (VP)  sponsored by NSF / Division of Advanced Scientific General and scientific explanation of human abilities Computing and limitations versus goal oriented use of visual perception in complex information presentation. Goal of committee  recommendations to HW/SW builders to improve Art and Design (AD) scientific productivity Aesthetics and style versus expressiveness and effectiveness Result of committee  recommendations to research communities to develop1.5. History new concepts/techniques for "Visualization in Scientific Computing (ViSC)"One of the earliest examples of three-dimensional scientificvisualization was Maxwells thermodynamic surface, Solidifying goalssculpted in clay in 1874 by James Clerk Maxwell.[7]  workshop on "Visualization in Scientific Computing"  sponsors: e.g. NSF and NASA Key Publication: [3] 2. COMMON QUESTIONS AND CONCERNS The discussion is focused on the following questions: 1. What is the improvement in the understanding of the data as compared to the situation without visualization? 2. Which visualization techniques are suitable for ones data? Are direct volume rendering techniques to be preferred over surface rendering techniques? 3. Can current techniques, like streamline and particle advection methods, be used to appropriately outline the known visual phenomena in the system? This prefigured modern scientific visualizationtechniques which use computer graphics.[8] The success of visualization not only depends on the results which it produces, but also depends on the Notable early two-dimensional examples include the environment in which it has to be done. This environment isflow map of Napoleon’s March on Moscow produced by determined by the available hardware, like graphicalCharles Joseph Minard in 1869;[9] the “coxcombs” used by workstations, disk space, color printers, video editingFlorence Nightingale in 1857 as part of a campaign to hardware, and network bandwidth, and by the visualizationimprove sanitary conditions in the British army;[9] and the software. For example, the graphical hardware imposesdot map used by John Snow in 1855 to visualize the Broad constraints on interactive speed of visualization and on theStreet cholera outbreak.[9] size of the data sets which can be handled. Many different problems encountered with visualization software must beNeed and opportunity taken into account. The user interface, programming model, increased data rates from data input, data output, data manipulation facilities, and o measuring devices: e.g. space missions, other related items are all important. The way in which these medical instruments ("fire hose") items are implemented determines the convenience and o scientific computing: e.g. national effectiveness of the use of the software package as seen by supercomputer centers the scientist. Furthermore, whether software supports mature and cheap technology: powerful graphical distributive processing and computational steering must be workstations, color, sufficient memory and storage taken into account. [10]
  3. 3. 3. CLASSIFICATION OF TECHNIQUES 5.1.2. IsosurfacesClassification of visualization techniques is often based on This technique produces surfaces in the domain of the scalarthe dimension of the domain of the quantity that is quantity on which the scalar quantity has the same value, thevisualized, i.e. the number of independent variables of the so-called isosurface value. The surfaces can be coloreddomain on which the quantity acts, and on the type of the according to the isosurface value or they can be coloredquantity, i.e. scalar, vector, or tensor. according to another scalar field using the texture technique. The latter case allows for the search for correlation between Visualization techniques can also be divided into different scalar quantities.surface rendering techniques, and (direct) volume renderingtechniques. Surface rendering is an indirect geometry based 5.1.3. Cutting planestechnique which is used to visualize structures in 3D scalaror vector fields by converting these structures into surface This technique makes it possible to view scalar data on arepresentations first and then using conventional computer cross-section of the data volume with a cutting plane. Onegraphics techniques to render these surfaces. Direct volume defines a regular, Cartesian grid on the plane and the datarendering is a technique for the visualization of 3D scalar values on this grid are found by interpolation of the originaldata sets without a conversion to surface representations. data. 5.1.4. Orthogonal slicers 4. COLOR CODING It often occurs that one wants to focus on the influence ofColors and light are essential to visualization. Most only two independent variables (i.e. coordinates). Thus, thevisualization techniques contain a step in which data values other independent variables are kept constant. This is whatare mapped to colors to make the range of the data visible. the orthogonal slicer method does. For example, if the dataThe interpretation of results produced by these visualization is defined in spherical coordinates and one wants to focus ontechniques depends crucially on the mapping of data to the angular dependences for a specific radius, the orthogonalcolors because the human eye is more sensitive to some slicer method constructs the corresponding sphere. Noparts of the visible spectrum of light than to other parts and interpolation is used since the original grid with thethe brain may interpret different color patterns differently. corresponding data is inherited.There exist quite a few color coding systems like the knownRGB, CMY, and HSV systems. 5.1.5. Vector glyphs This technique uses needle or arrow glyphs to represent5. VISUALIZATION TECHNIQUES AND METHODS vectors at each data point. The direction of the glyph corresponds to the direction of the vector and its magnitudeThis chapter introduces the visualization techniques and corresponds to the magnitude of the vector. The glyphs canmethods. The focus is on 3D scalar and vector techniques, be colored according to a scalar field.because often data consists of 3D scalar and vector fields. 5.1.6. Streamlines, streaklines, and particle advection5.1. Surface rendering techniques This is a set of methods for outlining the topology, i.e. theThis section briefly describes a general set of 3D scalar and field lines, of a vector field. Generally, one takes a set ofvector surface rendering techniques. The first four starting points, finds the vectors at these points bydescriptions deal with scalar field techniques and the other interpolation, if necessary, and integrates the points alongtwo with vector field techniques. the direction of the vector. At the new positions the vector values are found by interpolation and one integrates again.5.1.1. Scalar glyphs The difference between streamlines and streaklines is thatScalar glyphs is a technique which puts a sphere or a the streamlines technique considers the vector field to bediamond on every data point. The scale of the sphere or static whereas the streaklines technique considers the vectordiamond is determined by the data value. The scalar glyphs field to be time dependent. Hence, the streakline techniquemay be colored according to the same scalar field or interpolates not only in the spatial direction, but also in theaccording to another scalar field. In this way correlations time direction.can be found.
  4. 4. 5.1.7. Textures 5.3.1. Flipbook animationThis is a technique to color arbitrary surfaces, e.g. those This is a well known technique. The generated images aregenerated by the isosurface techniques, according to a 3D displayed one after the other. Its name is attached to thescalar field. An interpolation scheme is used to determine thumbing or flipping through a series of images.the values of the scalar field on the surface. A colormap isused to assign the color. 5.3.2. Keyframe animation5.2. Volume rendering techniques For this technique one only has to generate so-called keyframes. Keyframes mark changes in the characteristicsVolume rendering techniques have been developed to of the motion. Interpolation techniques are used to generateovercome problems of the accurate representation of a set of images between two keyframes.surfaces in the isosurface techniques. In short, theseproblems are related to making a decision for every volume 6. CONCLUSIONSelement whether or not the surface passes through it and thiscan produce false positives (spurious surfaces) or false In such a rapidly evolving field, it is important to be awarenegatives (erroneous holes in surfaces), particularly in the of the latest developments, as well as those areas of ongoingpresence of small or poorly defined features. Volume research. Two important recent developments are of specialrendering does not use intermediate geometrical importance. The first has been the explosion of the internet.representations, in contrast to surface rendering techniques. The growth of the web has created a new medium by whichIt offers the possibility for displaying weak or fuzzy visualization can be presented with a high level ofsurfaces. This frees one from the requirement to make a interaction.decision whether a surface is present or not. The second recent (and continuing) development is theTwo implementations of volume rendering are Ray casting increase in speed of PCs, as well as the reduction in cost ofand Splatting. The two methods differ in the way the RGBA high end workstations. This is bringing the world ofvolume is projected onto the 2D viewing plane. Scientific Visualization to every possible user.5.2.1. Ray casting 7. REFERENCESFor every pixel in the output image a ray is shot into the [1] The Institute Of Electrical And Electronics Engineers (IEEE), “2009 IEEE Taxonomy” Version 1.01 Supplied as additional materialdata volume. At a predetermined number of evenly spaced 2009Taxonomy_v101.pdf.locations along the ray the color and opacity values are [2] Gitta Domik [domik@siggraph.org] “Tutorial on Visualization”obtained by interpolation. The interpolated colors and University of Paderborn - Germanyopacities are merged with each other and with the [3] McCormick, B.H., T.A. DeFanti, M.D. Brown (ed), “Visualization in Scientific Computing”, Computer Graphics Vol. 21, No. 6,background by compositing in back-to-front order to yield November 1987the color of the pixel. [4] H. Aref, R. D. Charles and T. T. Elvins, “Scientific Visualization of Fluid Flow”, in C.A. Pickover and S.K. Tewksbury (eds), Frontiers of5.2.2. Splatting Scientific Visualization, 1994,Wiley Interscience. [5] J. Foley and B. Ribarsky, “Next-generation Data Visualization Tools”, in Scientific Visualization, 1994, Advances and Challenges,This technique was developed to improve the speed of Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A.calculation of volume rendering techniques like ray casting, Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academicat the price of less accurate rendering. A projection is made Press. [6] N. Gershon, “From Perception to Visualization”, in Scientificfor every voxel and the resulting splats are composited on Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum,top of each other in back-to-front order to produce the final R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko,image. G. Nielson, F. Post, D. Thalmann , Academic Press. [7] James Clerk Maxwell and P. M. Harman (2002), “The Scientific Letters and Papers of James Clerk Maxwell”, Volume 3; 1874-1879,5.3. Animation techniques Cambridge University Press, ISBN 0521256275, p. 148 [link] [8] Thomas G.West (February 1999). "James Clerk Maxwell, Working inThese techniques simulate continuous motion by rapidly Wet Clay". SIGGRAPH Computer Graphics Newsletter 33 (1): 15–displaying images. The viewer is given the impression that 17. [link] [9] Michael Friendly (2008). "Milestones in the history of thematiche is watching a continuous motion. To achieve this cartography, statistical graphics, and data visualization" Supplied asimpression the graphical hardware needs image display rates additional material milestone.pdf.of at least 25 images per second, since otherwise motion [10] Scientific Visualization Laboratory, Georgia Tech “Scientificwill look shaky. Visualization Tutorial” [link]

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