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Effective visualization of timevarying data using cognitionbased principles


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Title: Effective visualization of timevarying data using cognitionbased principles

Effective visualization of time-varying data
using cognition-based principles
  • Alark Joshi
  • University of Maryland Baltimore County

  • "The purpose of computing is insight, not
    numbers", Richard Hamming (1962)

  • Datasets are getting larger
  • Scanners are getting better and generating higher
    resolution data
  • Experimental simulations are generating datasets
    that are in the order of terabytes and in some
    cases even petabytes
  • Data is increasingly time-varying (ultrasound,
    weather forecasting, fluid flow)

  • Higher resolution provides improved accuracy for
    decision making and increased insight into the
  • Visualizing such datasets is challenging due to
    their size as well as the time-varying nature
  • Current techniques rely on the users ability to
    visualize change over time

Application Domains
  • Challenges faced by experts in the following
    applications domains
  • Computational fluid dynamics
  • Medical visualization
  • Weather forecasting

Computational Fluid Dynamics
  • Computational fluid dynamics is defined as the
    study of the dynamics of flow
  • Wind flow over an airplane wing
  • Outflow from a nozzle into tank, turbines etc
  • Flow is measured or simulated to generate
    time-varying data
  • A single volume could easily have a resolution of
    2563 and there could possibly be hundreds or
    sometimes thousands of time-steps of data
  • The size of each volume coupled with the number
    of time steps increases the size of the entire
    dataset considerably.

Image credits http//ilona.uni-mb.si/hribersek/hm
Flow visualization
  • Current approaches to visualizing time-varying
    data for every timestep
  • Standard volume rendering techniques
  • Isosurface rendering

Image from Visualizing and Tracking Features in
3D Time varying datasets, Xin Wang, Ph.D. Thesis,
Flow visualization
  • Rely heavily on the user's ability to identify
    and track regions of interest over time.
  • Number of snapshots generated can be quite high
    (100-3000), requiring considerable effort to
    track features.

Image from Volume Tracking by D. Silver and Xin
Wang, 1996
Visualization of turbulent vortex data
Video courtesy Deborah Silver,
Flow visualization
  • In an experiment by Pylyshyn, it was found that
    observers can track a maximum of five
    independently moving objects at the same time.
  • Speed increases Performance decreases
  • Number of objects increases Performance
  • Time-varying datasets commonly have 20-30 features

Zenon W. Pylyshyn. Seeing and Visualizing, 2003.
Flow visualization challenges
  • Effectively tracking features over time
  • Visualizing motion of features using a single or
    a smaller subset of images
  • Better ways to understand inter-feature

Medical visualization
  • Datasets are getting larger in size due to high
    resolution scanners
  • A single CT/MRI scan consists of a slice of
    resolution 512x512 and 1024/2048 such slices
  • Finer resolution is better for radiologists and
    doctors to make informed decisions

Image credits - http//support.vitalimages.com/img
Medical visualization
  • Standard volume rendering techniques are used to
    visualize these datasets
  • These techniques are currently being used for
  • Virtual colonoscopy
  • Surgery and treatment planning
  • Diagnosis purposes
  • Interactivity in such techniques is greatly
    hampered by the size of the dataset

Image credits - http//www.lifespanmedical.com.au/
patients/v_colonoscopy.htm and http//www.medscape
Medical Visualization challenges
  • Change in Morphology
  • Monitoring the size of a tumor over an extended
    period to determine efficacy of radiation therapy
  • Radiation therapy is a non-surgical alternative
  • Involves 7 weeks of therapy usually given 5 days
    a week
  • Change in content of the tumor
  • Change in tumor vessel content over time
  • Change in the stromal content over time

Source http//www.ncbi.nlm.nih.gov/books/bv.fcgi?
ridcmed.section.6535 and http//familydoctor.org/
Weather visualization
  • Weather prediction is crucial to minimize human
    and financial loss
  • Data collected by domain experts is in the order
    of tens of gigabytes per day

Video courtesy http//www.research.ibm.com/weathe
Weather visualization
  • Expected local weather conditions during the next
    day or two are critical factors in planning
    operations and making effective decisions
  • Most application domains work in a reactive
    manner due to the lack of accurate predictions
    (transportation, local government, insurance and
    so on)
  • Accurate weather forecasts can be used to improve
    operational efficiency and safety

Weather visualization challenges
  • Study of hurricanes has become very crucial
  • Conditions that cause the transformation of a
    category 3 hurricane to a category 5 hurricane
  • Path prediction for evacuation purposes
  • Entrainment in a hurricane that reduces the
    intensity of a hurricane
  • Requires tools to explore and study the evolution
    and transformations undergone by a hurricane

Weather visualization challenges
  • Study the interactions and relationships between
    attributes like pressure, temperature, winds and
    so on
  • Visualizing important events is beneficial in the
    study of hurricanes
  • Opening of the eyewall
  • Change in attributes such as pressure, cloud
    water, winds at crucial time intervals, when a
    hurricane changes its category

Related Work
  • Flow (CFD) visualization
  • Samtaney et al 94
  • Silver and Wang 96, 97
  • Ma and Shen 00
  • Medical Visualization
  • Levoy 90, Cabral et al 94
  • Kaufman 96
  • Guthe et al. 02

Image credits Silver and Wang 96 Kniss et al
Related Work
  • Weather Visualization
  • Wilhelmson et al 90
  • Davis et al 01, 02
  • Illustrative visualization
  • Lu et al. 03
  • Rheingans and Ebert 01
  • Burns et al 05

Image credits Wilhelmson 90 and Burns 05
Related Work
  • Simplification
  • Clark 76
  • Luebke et al 02
  • Luebke and Hallen 01
  • Cohen et al 98

Image credits Cohen et al 97
Increased abstraction
  • Through illustration we have the potential to
  • Interpret physical reality
  • Distil the essential components of a scene
  • Accentuate the important information
  • Minimize the secondary details
  • Hierarchically guide the attentional focus

Image credits http//www.bartleby.com/107/illus38
Source V. Interrante, The Visualization
Handbook, 2004.
  • We propose the use of illustration-based
    principles to
  • Visualize Motion over time
  • Visualizing positions of older timesteps
  • Using cognition-based simplification to aid
    visualizing motion over time
  • Visualize Morphological Change
  • Visualize Change in attribute values

Illustrations to depict motion
  • Illustrators have been using techniques to depict
    change over time

Images from Edward MacCurdy. The Notebooks of
Leonardo Da Vinci, 1954 Kawagishi et al.,
Cartoon blur Non-photorealistic motion blur
Visualize Motion over time Illustration-based
  • Enable users to pick and track features of
    interest successfully
  • Visualizing the motion experienced by a feature
    over time using a single image (or a small set of
  • Augmenting animations of time-varying data
    visualizations with illustration-based cues

Line Ribbon based techniques
  • Illustrators have used line and ribbon-based
    techniques to convey motion

Flow Ribbons
Images from Kawagishi et al., Cartoon blur
Non-photorealistic motion blur and Understanding
Comics by Scott McCloud, 1994
Depicting past positions
  • A trailing silhouette shows past positions of the
  • Illustrators have often used techniques where
    they use a blurred, desaturated image to depict
    an older time step whereas a more brighter, more
    detailed image represents a newer time step.

Opacity-based technique
Strobe silhouettes
Image from Kawagishi et al, Cartoon blur
Non-photorealistic motion blur Understanding
Comics by Scott McCloud, 1994
Visualize Motion over time Cognitive
  • Cognitive simplification can aid
    illustration-based visualization
  • A multiresolution representation can trade
    cognitive overload and effective visualization
  • Preserving regions of importance in the process
    of simplification

  • Automatically building hierarchical model
    representations to balance visual fidelity and

Inspired by chapter on Model Simplification by J.
Cohen and D. Manocha in the Visualization
Handbook, 2004. Image credits D. Luebke, A
Developers Survey of Polygonal Simplification
Algorithms. IEEE Computer Graphics Applications
(May 2001).
Cognitive Simplification
  • Preprocessing datasets to identify cognitively
    significant features
  • Generating simplified representations of the
    dataset that maintain those features
  • Reduce visual clutter and draw the users
    attention to regions of interest

Path abstraction
  • Speedlines technique requires an
    illustrative-simplification of the path followed
    by the feature

Unsimplified path Naïve
path Cognitive path
simplification simplification
Faithful to the path, connecting alternate
positions, connecting one of four positions
Shadow Simplification
  • Shadows provide increased depth cues and
    communicate spatial relationships Hubona 99
  • Experiments have shown that soft shadows cast by
    a mesh simplified to 1 its original size was
    acceptable to 90 of its users (Sattler 05)
  • Illustrative visualization will be augmented with
    simplified shadows to convey depth and
    orientation cues

Image from Exploitation of human shadow
perception for fast shadow rendering by Sattler
et al, 2005
Silhouette Simplification
  • We propose to simplify the silhouette based on
    its age
  • Older timesteps have a low detail silhouette and
    vice versa

Image from Kawagishi et al, Cartoon blur
Non-photorealistic motion blur
Partially-obscured feature simplification
  • In the flow ribbons technique, underlying
    features were partially-obscured by the ribbons
    to convey motion
  • Illustrators provide abstract representations of
    obscured features

Image from Understanding Comics by Scott McCloud,
Change Visualization
  • Visualizing change over time is crucial to
    understanding and studying a time-varying dataset
  • We propose to identify new techniques to
  • Change in morphology
  • Growth over time in a single image
  • Change in attribute values (like pressure,
    temperature etc.)

Morphological change
  • Effectively convey change in structure or form of
    a feature using a single image (small subset of
  • Tumor growth and treatment planning
  • Transformation undergone by a fluid flow feature
    (bifurcation, amalgamation, dissipation,

Morphological change
  • Visualizing the change (growth) undergone by a
    feature of interest

Significant snapshots
Image credits http//catalog.nucleusinc.com/genera
Change in Attribute values
  • Representing the change in the values of an
    attribute in a single image (small subset of
  • Change in value of pressure, wind speed in
    regions of the hurricane
  • Increase in the content of dry air in the eye of
    the hurricane (can drastically reduce the
    intensity of a hurricane)
  • Change in the content of a tumor over time
    increase in the stromal content of the tumor cells

Change in Attribute values
  • Using color, texture based methods to convey
    change over time
  • Illustration-based approaches

Image credits Temporal visualization of planning
polygons for efficient partitioning of
geo-spatial data, Shanbhag, P. Rheingans, P.
desJardins, M, 2005 Flow map layout, Doantam
Phan Ling Xiao Yeh, R. Hanrahan, P. Winograd,
T, 2005.
Plan of Work
Expected Research contribution
  • Illustration-based techniques to depict motion
  • Novel techniques for feature tracking
  • Cognitively simplified multiresolution
    representations of data
  • Illustration-based techniques to visualize change
    in morphology and attribute values

Expected Research Contribution
  • Flow visualization
  • Improved feature tracking using
    illustration-based techniques
  • Medical Visualization
  • Novel techniques to visualize change in structure
    and growth over time
  • Improved illustrative rendering with shadows
  • Weather Visualization
  • Cognitively simplified multiresolution data for
    effective visualization
  • Change in attribute values for accurate
    prediction and decision making

  • Next-generation tools will need to employ more
    ingenious approaches including more
  • sophisticated data models,
  • Multiresolution techniques,
  • Level-of-detail views,
  • Hierarchical data representation,
  • Region-of-interest rendering, ...
  • Don Middleton, Tim Scheitlin, National Center
    for Atmospheric Research and Bob Wilhelmson,

  • Dr. Deborah Silvers group at Rutgers University
  • Dr. Lynn Sparling, Dr. Miodrag Rancic, Hai Zhang
    at the Physics Department at UMBC
  • Scott McCloud, Kunio Kondo and Harper Collins
    Publishers for the Illustrations
  • This work has been funded by NSF grant numbers
    0121288 and 0081581.

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