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Research Statement NPR Approaches to Uncertainty Visualization

Research Statement NPR Approaches to Uncertainty Visualization
Research Statement NPR Approaches to Uncertainty Visualization

Research Statement:

NPR Approaches to Uncertainty Visualization

Kristin Potter

Introduction

The goal of visualization is to create a visual representation of complex information in order to gain insight and understanding.Often information is too complex to allow a simple understanding of the raw data,and comprehension of relationships and other insights is almost impossible.Representing the data in a way that allows a visual investigation uses the talents of the human visual system to more fully comprehend data.However,most visualizations do not take into account error or uncertainty in the data,information that is often displayed in2D as graphs and charts.This additional information is crucial in the thorough understanding of the data,the absence of which can lead to misrepresentations and false conclusions. The addition of uncertainty and error information into visualization is relatively new,however uncer-tainty and error is pervasive throughout the whole visualization process.Sources of uncertainty include data acquisition,sampling,quantization,interpolation,and the visualization itself.The location and mag-nitude of this uncertainty are important variables in some situations,and the lack of this information in a visualization can mislead the viewer.For this reason,the addition of such information is an important issue to address in thee visualization?eld.

In this research,I propose to address the problem of adding uncertainty and error information into visualizations.The results should be visually pleasing and highly informative.Once major problem that will be addressed is how to incorporate additional information into a visualization without adding clutter which may be visually distracting and confuse the viewer.I would also like to maintain the intuitiveness of the visualization by using illustrative techniques that convey meaning,rather than previous approaches that simply map uncertainty to a free rendering variable.My approach is to use non-photorealistic rendering techniques that simulate artist techniques in order to solve the problems of creating a visualization that is highly informative,visually pleasing,not distracting,and most of all,intuitive.A really great motivator to use NPR techniques is that a lot of visualization renderings are highly realistic looking which can lead to viewer to perceiving these images as truth(i.e.that’s what I look like on the inside)when in actuality, these lighting models are put in place to make the images look good,rather than re?ect how the data looks in real life.Thus,mapping to color and texture,while it is an easy and fast approach may mislead the viewer even more.But,using illustration type techniques(i.e.lines,silhouettes,sketchy stuff)the user is more likely to interpret the data in abstract terms and not as quickly accept the image as truth.

In?uential Work

The5most in?uential papers for this work are:

?Approaches to Uncertainty Visualization[Pang et al.1997]

?Procedural Annotation of Uncertain Information[Cedilnik and Rheingans2000]

?A Next Step:Visualizing Errors and Uncertainty[Johnson and Sanderson2003]

?I need at least one NPR in Visualization ref.

?Probably another NPR would be good.

Previous Work

The previous work focused on in this section will cover previous work done to integrate uncertainty and error into visualizations,as well as work in non-photorealistic rendering used in the visualization commu-nity.

The previous work focused on in this section will cover previous work done to integrate uncertainty and error into visualizations,as well as work in non-photorealistic rendering used in the visualization Uncertainty Visualization

A primary goal of visualization is to convey all relevant data to the viewer[Johnson and Sanderson2003]. Uncertainty and error of data are important aspects of a data set and are often overlooked during visualiza-tion due to the dif?culties of actually rendering these values in a manner that is informative and concise. In order to reveal as much truth as possible,uncertainty and error information should be included in the vi-sualizations[Wittenbrink et al.1995].Typically,error and uncertainty of data are presented as2D graphs but left out of2D and3D visualizations.A major reason for this is the dif?culty in expressing these ideas in a visually pleasing and informative manner.Methods of visualizing error and uncertainty include approaches that overload traditional visualization techniques by mapping free variables to uncertainty in-formation,as well as new techniques that derive visualization tools speci?c to uncertainty data.

Scienti?c visualization is a technique that uses visual representations to quickly communicate informa-tion.An effective visualization will express the information in a manner that gives the viewer a complete and accurate understanding of the data[Johnson and Sanderson2003;Wittenbrink et al.1995],as well as allow the viewer to establish relationships and insights that might not be apparent in other representations of the dataset.However,most visualizations leave out information about uncertainty and error that can in-?ltrate the dataset in a variety of ways,and drastically effect the interpretation of a visualization.This type of information is often presented as2D charts and graphs,and the integration into3D visualizations can be dif?cult.Besides the general problem of uncertainty information being overlooked during the creation of visualizations and the development of visualization techniques,the addition of uncertainty information can easily complicate a visualization,leading to distortions in the data representation,distract the viewer from the main goal of the visualization,emphasis uncertainty and error,clutter the visualization,and generally lower the visual appeal of the visualization.

0.1Comparison Techniques

Often uncertainty describes a comparison that can most clearly be understood visually,such as the differ-ence between surfaces generated using different techniques,or a range of values that a surface might fall in.A simple approach to the visualization of this type of information is a side-by-side comparison of the datasets.This however may not easily reveal subtle differences as the data may be nearly the same,and the more complicated the visualization,the harder it is to perform this task.Another simple approach is to overlay the data to be compared[Jospeh et al.1999],and the addition of transparency or wireframe can

produce a concise,direct comparison of the datasets.In a similar approach difference images can be used to speci?cally display areas of variation[Wittenbrink et al.1995].Often however,the uncertainty can be categorized as more of a range of values,rather than just two distinctive ones.For this,a surface sweep, known as a fat surface[Pang et al.1997]can be used to indicate all possible values.The integration of isosurface and volume rendering is another approach in which an opaque isosurface can be used to indicate the most likely value,and a transparent volume rendering can surround the isosurface to indicate the range of possible values[Johnson and Sanderson2003].Finally,bounded uncertainty,while not effectively vi-sualized in3D,can be expressed through the ambiguation of boundaries and edges of pie charts,error bars and other2D abstract graphs[Olston and Mackinlay2002].

Attribute Modi?cation

A standard method to visualize uncertainty is to map it to free variables in the rendering equation,or modify the visual attributes of the data.Approaches that fall into this category include texture,color,and opacity mapping.

One of the easiest parameters to use for uncertainty visualization is color.Often,opacity is also mod-i?ed based on uncertainty to create a more visually pleasing representation,or to distinguish areas of uncertainty.Methods to do this include modifying the bidirectional re?ectance function to change surface re?ectance,mapping uncertainty to color through a2D transfer function,or pseudo-coloring using a look-up table[Pang et al.1997].This technique has been used as a speci?c means for conveying uncertainty in the areas of volume rendering[Djurcilov et al.2002]and isosurfacing[Jospeh et al.1999;Rhodes et al.2003],and is often combined with other uncertainty visualization methods.A speci?c example of this technique colormaps?owline curvature onto volume rendered surfaces,highlighting areas in which small changes in isovalue lead to large changes in isosurface orientation,thus indicating areas where the isosurface is a poor representation of material boundary[Kindlmann et al.2004].Texture can also be used to convey uncertainty in a similar way as color and too is often modi?ed by opacity,hue or texture irregularities[Interrante2000].

Glyphs

A glyph is a symbol used to encode data values.Speci?cally,glyphs encode this data through orientation, size,color,location,and population.With respect the uncertainty visualization,glyphs can be used in a number of ways.Adding glyphs as the mode to convey uncertainty information is the simplest method, an example of which is adding vertical lines onto a surface,the length of which conveying the difference between two interpolation techniques.Another approach is to modify the existing glyphs used to visualize the data so as to encode both the data nd uncertainty information.An example of this technique is the use of glyphs to indicate direction in a?ow visualization,and modifying the length of the glyphs based on differences between integration methods[Pang et al.1997].The use of glyphs for the visualization of uncertainty information in vector?elds is presented in[Wittenbrink et al.1996].Here,new glyphs were created that convey both direction and magnitude information from the data,as well as indicate the uncertainty of the data.These glyphs use area,direction,length,and extra lines to convey all relevant information.

Glyphs are symbols used in visualization that signify data through parameters such as location,size, shape,orientation,and color.The multivariate nature of glyphs easily lend them to uncertainty visual-ization by mapping uncertainty to a free parameter.Such an approach would modify attributes of glyphs already present in the visualization,or independently add glyphs that speci?cally convey uncertainty.

An example of the?rst approach is presented as a procedural generation algorithm in work by Cedilnik and Rheingans[Cedilnik and Rheingans2000].In this work,the data is sampled on a regular grid and the size,color,and placement of glyphs are taken directly from the data samples.The uncertainty is then used to distort the glyphs such that glyphs with low uncertainty are very sharp,and the sharpness level decreases as the uncertainty level increases.This distortion is an intuitive indication of uncertainty and error while not placing heavy emphasis on areas of high uncertainty.However,care must be taken to not distort the glyph to the point of unreadability,and the glyph must not be distracting.This is done by ensuring that the amount of energy carried by each each glyph is normalized,giving the overall visualization a homogeneous feeling.

Often,the addition of glyphs to speci?cally convey uncertainty information is a preferable approach since not all data is visualized effectively using glyphs.A speci?c example is the UISURF system[Jospeh et al.1999]which visually compares isosurfaces and the algorithms used to generate them.In this system, box glyphs are used to express the difference between isosurfaces.The height of the box glyphs indicates the magnitude of the difference,and the sign of the difference is used to decide color as well as whether the box is displayed as the normal or inverse normal to the surface.Another use of glyphs expresses the volumetric differences between isosurfaces as spherical glyphs with scaled radii placed on grid points. Similarly,line,arrow,and spherical glyphs can be used to depict uncertainty in radiosity solutions,inter-polation schemes,vector?elds,?ow solvers,and animations by varying the placement,magnitude,radii, and orientation[Pang et al.1997].

While the use of glyphs are an effective way to express uncertainty,overloading standard glyphs cre-ates complex visualizations that may be hard to decipher and data and uncertainty may become confused.

A way to ease this problem is to use new glyphs that are designed to visually integrate uncertainty in-formation with the data[Wittenbrink et al.1996].The design of these new glyphs use standard glyph symbols such as arrows,adding extra lines and augmenting width,thickness and placement based on un-certainty and the desired glyph density.The overall effect of these new glyphs is a visually pleasing data representation that is easy to decode.

0.2Image Discontinuity

Scienti?c visualization heavily relies on attributes of the human visual system to create effective data representations.One attribute that is relied on heavily in uncertainty visualization is the ability for the eye to quickly pick up discontinuities in the image,and to interpret these discontinuities as areas with distinct data characteristics.Techniques that utilize discontinuities use surface roughness,blurring,and oscillations[Wittenbrink et al.1995],depth shaded holes,noise,and texture[Djurcilov et al.2002],and the translation,scaling,rotation,warping,and distortion of geometry already used to visualize the data [Pang et al.1997]to visualize uncertainty.

An interesting approach uses annotations already present in the visualization,such as latitude and lon-gitude to express uncertainty[Cedilnik and Rheingans2000].In this work the annotations,which are generated procedurally,are modi?ed to indicate uncertainty,while maintaining a perceptual normalization and conveying inherent meaning.Speci?cally,the width,and brightness of the annotation lines are varied based on uncertainty,but the overall energy present is the same across the image.Thus,in areas of high uncertainty,the annotation lines are wide with a low brightness level,while low uncertainty is expressed through thin,bright lines.Similarly exponential sharpness can be varied to create lines that are dim with soft edges in areas of high uncertainty,while low uncertainty areas have sharp,bright lines.Besides width, sharpness,and brightness modi?cation,noise can be used to make the lines appear fuzzy or patterns such wavy lines are options to express uncertainty.All of these methods allows the level of uncertainty in the

dataset to be easily deciphered,areas of high uncertainty do not attract the eye,and the expression of uncertainty does not conceal the data from the viewer.

0.3Animation,Soni?cation,and Psychovisual Approaches

While the standard techniques of uncertainty visualization are presented in the above sections,research has been done to visualize uncertainty information through animation,soni?cation and psychovisual ap-proaches.The use of animation in uncertainty visualization can be used as a way to simplify the visual-ization and unify areas of like uncertainty by not displaying all of the information at once,but animating glyphs and icons from lowest to highest levels of uncertainty[Jospeh et al.1999].Also,uncertainty can be mapped to animation parameters such as speed,motion blur,duration,and range of motion[Pang et al.1997].Soni?cation is a technique that maps uncertainty to sound parameters such as pitch,duration, timbre,and volume[Pang et al.1997].And?nally,psychoviusual approaches such as stereo pairs and sub-liminal messages have been attempted to express uncertainty[Pang et al.1997],however the effectivness of this type of approach is yet to be seen.

Non Photorealstic Rendering in Visualization

Add in a summary of Ebert’s Slides,plan on going into detail in the proposal.

Proposed Work

The work I propose to do focuses on the creation of visualizations that incorporate uncertainty,error and con?dence information.These visualizations will have the following two requirements:1)convey the additional information in a clear,intutive manner and2)maintain a high level of visual quality.I will be using non-photorealistic rendering techniques to achieve these goals and plan to look at three speci?c visualization exmaples that would bene?t from the addition of uncertainty information.These examples will be from the areas of isosurfacing,volume rendering,and possibly?ow visualization. Random Thoughts

A major question that has to be asked when trying to visualize uncertainty is“What is the goal of the visualization?”and“How is adding uncertainty going to add to that goal?”.A successful visualization has a speci?c purpose,which may or may not need all of the information available about the data.If the purpose is strictly to show a general feel of a dataset,or give the user an idea of what the data looks like, then visualizing all known information may be too complicated a task requiring too much time to create the visualization,as well as give the user way too much information.However,if the user is supposed to get highly detailed information from a visualization,then the extra work put into the image is reasonable, and needing the user to spend more time deciphering the image is also acceptable.Thus,the creator of the visualization must know the needs of the audience as well as fully understand her creation goal.

Possible Problems to Solve

An NPR Approach to Isosurface Rendering with Uncertainty

Current isosurface rendering techniques that incorporate uncertainty use color and texture as free variables to map uncertainty values.These parameters may fool and distract the eye,are not visually pleasing,and are not a very intuitive way to express uncertainty.

In General,what are good ways to intuitively express uncertainty? Mapping uncertainty to color and texture is not necessarily an intuitive approach to uncertainty visualiza-tion.It would be nice to use techniques that can convey meaning,rather than just using free parameters.

1Relevant Areas of Research

?Uncertainty Visualization

?Overview of Visualization as a Field

?NPR used in Scienti?c Visualization

?NPR in general

?Handmade Visualizations

?Information Visualization

2Questions to Ask

?What kind of data are we going to look at?(Be speci?c)

?Who is the target audience of these images?

?How well do these images work for the target audience?

?Are these images artistically pleasing?How can they be more so?

?How do we evaluate how“good”these images are?

[Wittenbrink et al.1995]

3Problem Statement

In this work,I propose to have the overall goal of visualizing uncertainty in a way that is as intuitive to the viewer as possible.I would like to create images that quickly convey information in a visually pleasing manner.I plan on using non-photorealistic rendering techniques in order to simulate the artist’s hand,in hopes to easily bring the eye to areas of uncertainty,express in a pleasing manner the amount and type of uncertainty,all the while maintaining the overall feel of the image.

4Pre-Proposal Outline

data interpretation

4.1Introduction

The role of visualization is to accuratly express data in a means that is quickly understanable to the viewer. Most of the work done in the visualization?eld focuses on how to best represent the data visually,however the uncertainty and errors inherent to the data are often not represented in these visual presentations.The goal of this work is to add a visual representation of uncertainty,con?dence,error,or importance into a scienti?c visualization.I propose to use non-photorealistic rendering techniques that will indicate the uncertainty in the data in a more intuitive manner.

4.1.1Introduction to Visualization/Motivation to Visualize

Visualization is a means of coveying information that is not readily decipherable into a visual representa-tion.The main goal of visualization is present data in such a way as to allow the quick interpretation of the data.Visualization can also enhance the understanding of data and information by presenting it so that re-lations etc are explicit in the visual representation.Visualization as a?eld has a large base of research that covers various methods and techniques for visualizaing various forms of data and information.However,

a major missing part is the visualization of uncertainty,error etc.

4.1.2Introduction/Motivation to Unertainty Visualization

While[Johnson and Sanderson2003]

4.1.3Motivation of this Work

Previous work in uncertainty visualization focuses on the representation of uncertainty by mapping to a free varible in the rendering.This includes mapping to color,opacity,texture and others.However,these approaches often lead to confusing visualizations that are not at all intuitive.The mapping of colors to uncertainty requires the viewer to decode the color mapping,whereas mapping values of like temperature to color is very intuitive.

Brief overview of the problems with current uncertainty visualization techniques.

4.2Previous Work

4.2.1Overview of Uncertainty Visualization 6.2.1.1Scalar Approaches

6.2.1.22D Approaches(Isosurfaces)

6.2.1.33D Approaches(V olume Rendering)

4.2.2NPR in Visualization

4.3Problem Statement

Make uncertainty visualization intuitive so that we don’t have to read paragraphs of explanation to?gure

out how to read the image.

5Emails and Correspondences

Kristi,

This is a great start.My assumption is that you are writing the summaries,yes?That is a great way

to get your background chapter written.I would read all of the stuff on the UCSC webpage and add it to

your page.Then,David Ebert and Viola et al.are doing an NPR visualization course at Eurographics.

Ping David to see if you can get a copy of the course notes,that should give you all of the relevant NPR-

VIS background reading.While you are reading write down any problems that you can think of that are

not being solved using current systems.At this point of the research/dissertation process?nding good problems is far more important than?nding good solutions.

Also,check the following link out,I think it has some good ideas for your motivation.

https://www.wendangku.net/doc/2110601518.html,/Faculty/Nature_Commentary_Jan03.pdf I would make the following changes to your statement.

The goal of this work is to add a visual representation of uncertainty,con?dence,error,or importance

into a scienti?c visualization using non-photorealistic rendering techniques.The results will be evaluated

in both the scienti?c and educational tasks.

Bruce.

References

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with https://www.wendangku.net/doc/2110601518.html,puters and Graphics26,239–248.

I NTERRANTE,V.2000.Harnessing natural textures for multivariate visualization.IEEE Computer Graphics and Applications20,6(November/December),6–11.

J OHNSON,C.R.,AND S ANDERSON,A.R.2003.A next step:Visualizing errors and uncertainty.IEEE Computer Graphics and Applications23,5,6–10.

J OSPEH,A.J.,L ODHA,S.K.,R ENTERIA,J.C.,AND P ANG.,A.1999.Uisurf:Visualizing uncertainty

in isosurfaces.In Proceedings of the Computer Graphics and Imaging,184–191.

K INDLMANN,G.,W HITAKER,R.,T ASDIZEN,T.,AND M OLLER,T.2004.Curvature-based transfer functions for direct volume rendering:Methods and applications.In Proceedings of the14th IEEE Visualization2003(VIS’03),67–74.

O LSTON,C.,AND M ACKINLAY,J.D.2002.Visualizing data with bounded uncertainty.In Proceedings

of the IEEE Symposium on Information Visualization(InfoVis’02),37–40.

P ANG,A.,W ITTENBRINK,C.,AND L ODHA.,S.1997.Approaches to uncertainty visualization.The Visual Computer13,8(Nov),370–390.

R HODES,P.J.,L ARAMEE,R.S.,B ERGERON,R.D.,AND S PARR,T.M.2003.Uncertainty visualiza-tion methods in isosurface rendering.In EUROGRAPHICS2003Short Papers,83–88.

W ITTENBRINK,C.,P ANG,A.,AND L ODHA,S.1995.Verity visualization:Visual mappings.Tech.rep., University of California at Santa Cruz.

W ITTENBRINK,C.M.,P ANG,A.T.,AND L ODHA,S.K.1996.Glyphs for visualizing uncertainty in vector?elds.IEEE Transactions on Visualization and Computer Graphics2,3(September),266–279.

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