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Visualization and Persuasion
Amitabh VarshneyDepartment of Computer Science
University of Maryland at College Parkhttp://www.cs.umd.edu/~varshney
Slide 2
The Biggest Challenge of Our Era Data and Information Overload
Data produced during 2003 – 2005 exceeds all of thepreviously created data by mankind
… and 90% of the new data is digital
Acquisition:• Audioscapes, Image and Video Archives• GIS (satellites to low-flying planes), Laser Scanners• Medical Imaging (CT, MRI, fMRI, …)• Molecular Imaging (NMR, Crystallography, Microarrays, …)
Generation:• Meteorology: Atmospheric simulation and prediction• Biology: Molecular dynamics, protein docking• Space Sciences: Solar wind turbulence, micro-gravity fluid mechs.• High-Energy Physics: Multi-scale Plasma Dynamics
Slide 3
UMD CPU-GPU Cluster• 15 Compute Nodes and 13 Render Nodes• 4 Nodes for Scheduling and File Server• 1 User node (keyboard, terminal)• Each node has:
– Dual 3GHz Intel Xeon CPUs– NVIDIA GeForce 7800 GTX GPU– 8 GB RAM– 100 GB Disk
• Parallel fileserver has 10 TB disk storage• Network interconnect: 10 Gbps Topspin
Infiniband
Slide 4
UMD Active Tiled Display Wall
Total resolution: 50 mega pixels(5 x 5 array of 1920 x 1200 Dell 24” LCD screens)Ultrasonic air-mouse interface
Slide 5
Rhetorics
Rhetoric is the faculty of discovering in any particularcase all of the available means of persuasion
– Aristotle (Book I)
• Aristotle assumed that the agent of persuasion is the oratorand the medium of persuasion is the language
• Vast number of elements in any discussion
Rhetor’s ultimate goal, whenever possible, is to makethe most relevant object, concept, or value fill theaudience’s entire “field of consciousness”.Perelman & Olbrechts-Tyteca, The New Rhetoric: A Treatise on Argumentation, 1971
Slide 6
Communication – Language vs Visual
• Language: linear, apprehended slowly, systematicprocessing
• Images: Comprehended wholistically, instantaneously,emotional processing
• Images (or image-evoking language) are powerful,precisely because they provide superior opportunities todominate the field of consciousness
Slide 7
Visual Persuasion
• Advertising
• Criminal-Justice System
• Political Campaigns
• State Propaganda
• Religion, and many others …
… touches every part of our lives.
Most rely on Aristotle’s pathos, sometimes on ethos,seldom on logos
Slide 8
Visual Persuasion in Geology
from The Map That Changed the World by Simon Winchester
William Smith (1769 – 1839)
• Observed stratification of rocks during his work on coal mines and later canals
• The order of rocks were the same
• The depths at which they occurred differed
• Hypothesized a 3D structure of layers at various slopes beneath the Earth’s surface
• Used measurements along canals, mine shafts, and extrapolated…
Slide 9
Visual Persuasion in Geology
from The Map That Changed the World by Simon Winchester
• The first systematic spatialstudy of various rock strata
• Used fossil types in variouslayers of rocks to fine-tune
• Created the first geologicalmap in 1815•Visually persuaded the world ofthe innate structure of theEarth; immensely influential in:
•Geology
•Natural History and Biology
•Mining coal, oil, iron, diamond,platinum, silver, …
Slide 10
Visual Persuasion in GeoPhysics
From Pitman and Heirtzler, Science, Dec 1966
Magnetic Profile and Bathymetry data from USS Eltanin, track 19
Slide 11
Visual Persuasion in GeoPhysics• The first visual proof of symmetry of magnetic anomalies• Visually persuasive evidence leading to the acceptance ofthe theory of sea-floor spreading
Poster source: NOAAImage from: The Earth by Tarbuck and Lutgens
Slide 12
Visual Persuasion in Music
Beethoven’s Für Elise
Mary had a Little Lamb
Arc Diagrams by Martin Wattenberg, IEEE InfoVis 2002
Slide 13
General Observations• Common amongst examples
– Carefully hand-crafted– Took immense effort– One of a kind
• General principles:– Results convey important elements in a clean and
visually consistent way– They emphasize salient features and suppress others– They are parsimonious visualizations and they make
the few resources work extra hard (space, color, …)• Building Blocks
– Visual attention– Data salience
Slide 14
Visual Attention A way of seeing is also a way of not seeing – a focus on
object A involves a neglect of object BKenneth Burke
• Where we look has significant implications for:– what we perceive– how we interpret– how we act
• Visual attention is the primary filter by which we can copewith our immense sensory bandwidth– Retinal information is too vast and most of it has no survival value
• Eye-tracking can quantify overt visual attention
Slide 15
What Controls Visual AttentionBottom-up Image Properties (task independent)
Top-down Semantics and Task-driven Properties
[Yarbus 1967]
[Yarbus 1967]
Slide 16
Related Work• Image saliency maps
– Tsotsos et al. 95, Milanese et al. 94, Itti et al. 98, Rosenholtz 99
• Applications: compression and cropping– Privitera and Stark 99, Chen et al. 03, Suh et al. 03
Itti et al. PAMI 98
Suh et al. UIST 03
Saliency-basedcropping
Without cropping
Slide 17
Distinctive 3D structure pops out pre-attentively
Saliency has a 3D Component
Based on Enns and Rensink 90
3D featurespop out quickly
2D featuresnot pre-attentive
Slide 18
Mesh SaliencySaliency should find regions that aredifferent from their surrounding context
Curvature SaliencyArmadillo Leg
Lee, Varshney, Jacobs, SIGGRAPH 2005
Slide 19
Mesh SaliencySaliency should capture interestingfeatures at all meaningful scales
Saliency at a large object scale Saliency at a small object scale
Slide 20
Mesh Saliency
Saliency Computation Overview
Curvature Saliency Maps at multiple scales
NonlinearNormalization
Center-Surround
Slide 21
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Slide 22
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Slide 23
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Slide 24
Mesh Saliency Results
Stanford Armadillo Cyberware Isis
Slide 25
Visual Computing• The process of transforming data to images involves
– Dimension explosion – add normals, reflectance, camera,lighting, transfer functions, …
– Dimension reduction – incorporate everything to give us a grid ofcolors and their locations
• How is data salience propagated through such apipeline?– Are we certain it survives?
• Every communication has a message– And every point and every voxel a default importance
• How can we preserve data salience in rendered images?
Slide 26
Persuasive Visualization• Define Salient
– Human– AI assistant
• Identify Salience– Features– Patterns– Coherence– Interactions
• Preserve it through the Visual Computing pipeline:– Camera– Geometry / Data Representation– Transfer Functions– Lighting– Texture, Color, Glyphs
• Validate in final rendering
Slide 27
Simplification: Scale the quadric error by the saliency topreserve more triangles for salient regions
Viewpoint Selection: Find the viewpoint that maximizes thesum of the visible saliency• Gradient-descent-based optimization
Cyberware Male Mesh Saliency
Saliency Applications
Slide 28
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Slide 29
Preserving Salience• Transfer Functions: Modulate opacity/transfer
functions [Groeller et al. TVCG 04 –VisSym 07]
• Rendering Stylization: [Kim and Varshney]
Original Suggestive Contours SC weighted by Saliency
Slide 30
Lighting in Photography
from Envisioning Science by Felice Frankel
Slide 31
Visual Continuity & Consistency• Shots often composited from multiple takes
• Lighting is painstakingly made consistent
• Consistent lighting considered important for visualcontinuity and storytelling
Slide 32
Is Consistent Lighting Necessary?• Nature has one dominant light source
• Evolution might have endowed us with anability to discern inconsistency in illumination
• Just as it has inconsistency in perspective
Analysis of Perspective by Chris Tyler, Smith-Kettlewell Institute
Slide 33
Illumination Inconsistencies
Illumination inconsistencies are not perceptually salientOstrovsky, Sinha, Cavanagh, Perception 2006
Recent research suggests that illuminationconsistency is not resolved at the low-levelhuman vision
Find the cube lit inconsistentlywith respect to others:
On average, users take 8 seconds to answer and are then wrong 30% of the time
Slide 34
George Washington Crossing the Delawareby Emanuel Gottlieb Leutze
Discrepant Lighting in Art
Slide 35
Discrepant Lighting in Art
The Music Lesson by Jan Vermeer
Slide 36
Light Collages: Basic Idea
Allow local lighting parameters to be definedindependently at local regions
Lee, Hao, Varshney, Visualization 2004, TVCG 2005
Slide 37
Light Collages Overview• Segmentation [Mangan & Whitaker 99]
• Light Placement andAssignment to patches
• Proximity Shadows• Silhouette Enhancement
3D Mesh Segmentation Light Placement& Assignment
SilhouetteEnhancement
ProximityShadows
Slide 38
Results – Pelvis & Skull
(a) Uniform 4 lights (b) Light Collages:with 4 lights
(c) Light Collages:Silhouettes+ Shadows
Slide 39
Manuscript courtesy of Paul Debevec, USC and XYZ RGB Inc.
1 uniform light 4 uniform lights Light Collageswith 4 lights
Results - Manuscript
Slide 40
Visual Persuasion• We can draw viewer attention in several ways• Obtrusive methods like arrows or flashing pixels
– Distracts the viewer from exploring other regions• Principles of visual perception used by artists
and illustrators– Gently guide to regions that they wished to
emphasize
Kim and Varshney, IEEE Visualization 2006
Slide 41
Visual Persuasion (Art)
The Feast of Belshazzar, Rembrandt, 1635
Slide 42
Saliency-Guided Visual Persuasion• Saliency Field• Enhancement Operators• Emphasis Field• Saliency Enhancement• Saliency-enhanced Volume Rendering• Validation by eye-tracking based user
study
Transfer Functions
Saliency Fieldby User Input
Emphasis FieldComputed
EnhancementOperators
Saliency-enhancedVolume Rendering
Validation by eye-tracking device
SaliencyEnhancement
Slide 43
Emphasis Field Computation
• Mesh Saliency: S (v) = G(C, v, σ) – G(C, v, 2σ)• We introduce the concept of an Emphasis Field E
to define a Saliency Field S in a volumeS (v) = G(E, v, σ) – G(E, v, 2σ)
Given a saliency field, can we design somescalar field that will generate it?
KnownUnknown
Known Unknown
Slide 44
Emphasis Field Computation• Expressible as simultaneous linear equations
• Saliency Enhancement Operator (C-1)– CE =S , which implies E = C-1S– Given a saliency field S , the enhancement operator C-1
will generate the emphasis field E
where cij is the difference between two Gaussianweights at scale σ and at scale 2σ for a voxel vjfrom the center voxel vi
=
Slide 45
Emphasis Field Computation• We like to use enhancement operators at multiple
scales σi– Let E i be the emphasis field at scale σi
– Compute this by applying the enhancement operator Ci-1 on
the saliency field S– Final emphasis field is computed as the summation of E i
Slide 46
Emphasis Field in Practice• A system of simultaneous linear equations in n
variables– Generally, can handle arbitrary saliency regions and values– Computationally expensive: O(kn2) or O(n3)
• Alleviate this by solving a 1D system of equations
– Given a saliency field– Solve 1D system of equations at
multiple scales and sum them up– Approximate results using
piecewise polynomial radialfunctions [Wendland 1995]
• Interpret results to be along the radial dimension– Assume spherical regions of interest (ROI)
Slide 47
Saliency-guided Persuasion• Previous Gaussian-based Enhancement of a Volume
– Volume Illustration [Rheingans and Ebert TVCG 01]– Importance-based regional enhancement
• Various rendering stylizations and effects possible– Brightness– Saturation
Slide 48
Visual Persuasion - Brightness
Traditional VolumeRendering
Gaussian-basedEnhancement
Saliency-guidedEnhancement
Traditional VolumeRendering
Gaussian-basedEnhancement
Saliency-guidedEnhancement
Slide 49
User Study• Validated results by an eye-tracking-based
user study• Eye-tracker ISCAN ETL-500
• Records eye movements at 60Hz
• Hypotheses: The eye fixations increase overthe region of interest (ROI) in a volume by thesaliency-guided enhancement compared to– the traditional volume visualization
(Hypothesis H1)– the Gaussian-based enhancement
(Hypothesis H2)
Slide 50
User Study – Results
Traditional Volume RenderingTraditional Volume RenderingWith Fixation Points
Saliency Field Gaussian-based EnhancementGaussian-based EnhancementWith Fixation Points
Saliency-guided EnhancementWith Fixation Points
Saliency-guided Enhancement
Slide 51
Data Analysis
The percentage of fixations on the ROI for the original,Gaussian-enhanced, and Saliency-enhanced
visualizations
Kim and Varshney, IEEE Visualization 2006
Slide 52
Concluding Remarks
• Computational models that mimic perceptualsalience for 3D objects
• Methods to persuasively guide and hold visualattention
• Data is always sacrosanct but visualization isnever neutral– View location, view orientation, lighting (number, and
types), reflectance, transfer functions, …• Every communication has a message
– Whether intentional or not
Slide 53
Concluding Remarks
• Every communication genre employs its distinctrhetorics– Movies vs documentaries– Posters vs newspapers– Photography vs art
• Towards a Language of 3D Visual Communication– Data: nouns– Visual Salience: adjectives– Visual Persuasion: verbs
Slide 54
Acknowledgements
Students: Youngmin Kim, Chang Ha Lee, David Dao, Rob Patro,Derek Juba, Cheuk Ip, Roman Stanchak
Colleagues: David Jacobs, Hamish Carr, Helwig Hauser, FrancoisGuimbretiere, Joseph Ja’Ja’, Chris Johnson, Arie Kaufman,
Raghu Machiraju, Torsten Moeller, Dianne O’Leary
Sponsors: National Science Foundation, DARPA, DTO-IARPA,Army Research Labs, Kitware