cse 564: visualization introduction - stony brook...

CSE 564: Visualization

Introduction

Kl M llKlaus Mueller

Computer Science Department

Stony Brook University

The Visual Brain

O 50% f th h b i i d di t d t i i dOver 50% of the human brain is dedicated to vision and visual representations,• decoding visual information• high-level processing of visual information• thinking with visual metaphors

Input Device: The Eye Sensor: The Cones and Rods

T t f t tiTwo types of receptors on retina: rods and cones

Rods:Rods:• spread all over the retinal surface (75 -

150 million)• low resolution no color vision but very• low resolution, no color vision, but very

sensitive to low light (scotopic or dim-light vision)

Cones:Cones:• a dense array around the central

portion of the retina, the fovea centralis (6 7 million)(6 - 7 million)

• high-resolution, color vision, but require brighter light (photopic or bright-light vision)bright light vision)

Wiring: The Visual Pathways Processing Unit: The Visual Cortex (V1, V2)

Vi l t b k i t i t diff t tVisual cortex breaks input up into different aspects: • color, shape, motion, depth

LGN= Lateral Geniculate Nucleus

Pre-Attentive Processing

If t it t f t l d t t dIf you want it or not: some features are always detected

And fast – within 200 ms or less


Wh i it f t?Why is it so fast?

Well, because 50% of the brain is dedicated to vision

Vision is a MASSIVELY parallel processor dedicated to• detect

l• analyze• recognize• reason with

visual input


S iti it t diff iSensitivity to differences in:• color, orientation, size, shape, motion, shading, 3D depth, …


B t th li it j ti d ’t k llBut there are limits: conjunctions don’t work well

quick: find the blue circle


S f t / t th thSome features/cues are stronger than others:


W d tt hi h f t tt ti f tWords are patterns, which form strong pre-attentive features• this would have been different if this had been done in Arabic

There are limits, however• let’s see the next experiment p


R di 1Reading 1

Aoccdrnig to a rscheearch at an Elingsh

uinervtisy, it deosn't mttaer in waht oredr the

ltteers in a wrod are, the olny iprmoetnt tihng

is taht frist and lsat ltteer is at the rghit pclae.

The rset can be a toatl mses and you cany

sitll raed it wouthit porbelm. Tihs is bcuseae

we do not raed ervey lteter by it slef but thewe do not raed ervey lteter by it slef but the

wrod as a wlohe


Now, is tihs ture? Raed on….


R di 2Reading 2

Anidroccg to crad cniyrrag lcitsiugnis

planoissefors at an uemannd, utisreviny

in Bsitirh Cibmuloa, and crartnoy to the

duoibus cmials of the ueticnd rcraeseh,

a slpmie, macinahcel ioisrevnn ofp ,

ianretnl cretcarahs araepps sneiciffut to

csufnoe the eadyrevy oekoolnrcsufnoe the eadyrevy oekoolnr


R di 2Reading 2

According to card carrying linguistics

professionals at an unnamed, university

in British Columbia, and contrary to the

dubious claims of the uncited research,

a slpmie, macinahcel ioisrevnn ofp ,

ianretnl cretcarahs araepps sneiciffut to

csufnoe the eadyrevy oekoolnrcsufnoe the eadyrevy oekoolnr


R di 2Reading 2

According to card carrying linguistics

professionals at an unnamed, university

in British Columbia, and contrary to the

dubious claims of the uncited research,

a simple, mechanical inversion ofp ,

internal characters appears sufficient to

confuse the everyday onlookerconfuse the everyday onlooker

What To Learn From This

Th h i l t (HSV) t l t ( i l) iThe human visual system (HSV) tolerates (visual) noise very well• it can read the randomly garbled text very well• machines (equipped with computer vision) are poor at this

Humans have only limited computational capacityh d t t fi d l t d i h t t• hard to execute a fixed rule to decipher text

• especially once the text gets longer (7±2 rule of working memory)• this is where computers excel

The fact that computers deal poorly with noisy patterns is exploited in CAPTCHA• CAPTCHA: Completely Automated Public Turing Test to tell• CAPTCHA: Completely Automated Public Turing Test to tell

Computers and Humans Apart• used to ensure that an actual human is interacting with a system• some examples:some examples:

- creating a new gmail or yahoo account (prevent spammer accounts)- submitting files, data, email

CAPTCHA

CAPTCHA i d tl di t t d tt th t diffi ltCAPTCHA: noisy and vastly distorted patterns that are difficult to recognize by machines

Completely Automated Public Turing testto tell Computers and Humans Apart

CAPTCHA: One More

A t i i l ith h bAs computer vision algorithms have become more sophisticated at CAPTCHA character recognition…• the latest approach is object recognition

Visualization Domains Visualization Examples

Visualization Examples Visualization Examples


Visual Analytics

A i fi ldA new emerging field

Visual Analytics = the science of reasoning with visual informationinformation

Pairs machine intelligence (computing, bit-representations) with human intelligence (creativity, visual representations)g ( y, p )

Recent Headline

Connecting The Dots… Connecting The Dots…

Connecting The Dots…

structured data unstructured data

other data…



other data… hypothesis:- connect by number



other data… hypothesis:- connect by number

It’s Not Always That Easy….

More data- structured

- unstructured

Will the hypothesis workWill the hypothesis work here?



- unstructured

Will the hypothesis workWill the hypothesis work here? No…



- unstructured



- unstructured

Slowly sculptSlowly sculpt..



- unstructured


other data…

He is white



- unstructured




- unstructured


Typical Data

salient feature

salient data point

outlier

Connecting The Dots: Cause + Effect

Observe data infer causality relations


Simple causal chain …. complex interactions


Complex causal network … yet vastly simplified (mind map)


Very complex causal network

Data

Oft t i b dOften present in abundance• what is relevant/irrelevant?• under what conditions/task?

Data


May be missing in critical areas• where are these areas?

d k h t d ’t k ?• do we know what we don’t know?

Data




Prone to uncertainties• acquisition errors noise ambiguities misinterpretationsacquisition errors, noise, ambiguities, misinterpretations, …

Data




Prone to uncertainties• acquisition errors noise ambiguities misinterpretationsacquisition errors, noise, ambiguities, misinterpretations, …

Complex interactions• not just “A causes B”: A Bj• but multivariate: wA1⋅A1 + wA2⋅A2 + … B

Data Interaction Model


Connects the dots• add structure to the data


Connects the dots• add structure to the data

information

Traditional Data Analytics: Human

dl i td t deepanalysis

pre-analysis reportdata

Traditional Data Analytics: Machine



Traditional Data Analytics: Either/Or



Comparison: The Good

Human MachineMemory Tremendous

Computational Bandwidth Highp gEndurance HighIntelligence High

Creative Thinking HighCreative Thinking HighPattern Recognition High

Comparison: The Bad

Human MachineMemory Limited

Computational Bandwidth PoorpEndurance LowIntelligence Limited

Creative Thinking LimitedCreative Thinking LimitedPattern Recognition Poor

Comparison: Complementing Strengths

Human MachineMemory Limited Tremendous

Computational Bandwidth Poor Highp gEndurance Low HighIntelligence High Limited

Creative Thinking High LimitedCreative Thinking High LimitedPattern Recognition High Poor

Visual Analytics: Exploiting Synergies



Visual Analytics: Exploiting Synergies

sense-making loopsense making loop



Human-Machine Analytical Discourse

E bl d h / hi i tiEnable good human/machine communication• key to synergistic problem solving



Visual communication has best bandwidth• highly parallel and noise-tolerant




Visual story telling with data & findings• needed: visual language rooted in data analyticsneeded: visual language rooted in data analytics




Visual story telling with data & findings• needed: visual language rooted in data analyticsneeded: visual language rooted in data analytics

Visual interface 2-way communication channel• interaction is key• gesture-driven human/machine discourse

Visualization of 1-3 Dimensional Data

2D i l f( )

1D signal f(x)

2D signal f(x, y)

4D i l f( ti )

2D signal, shown as height field

3D signal f(x, y, z)4D signal f(x, y, z, t=time)example: 3D heart in motion

High-Dimensional Data

C id th li t f t fConsider the salient features of a car:• miles per gallon (MPG)• top speed• acceleration• number of cylinders• horsepower• weight• year• country originy g• brand• number of seats• number of doors• reliability (average number of breakdowns)• and so on...

H d i ll l ti f ?How do we visually compare a selection of cars?• this is a high-dimensional visualization problem… how to do this?

High-D Visualization Example: Parallel Coordinates

a car as a 7-dimensional data point


a database of cars


after some clustering


with mean trend

Optical Illusions

C t th bl k d t !Count the black dots!

Optical Illusions

Optical Illusions Optical Illusions




Are the purple lines straight or bent?

Optical Illusions

Which circle in the middle is bigger?

Optical Illusions

Do you see gray areas in between the squares? Now where did they come from?

Optical Illusions

You should see a man's face and also a word...

Hint: Try tilting your head to the right, the world begins g , g

with 'L'

Optical Illusions: Sidewalk Art

Julian Beever


Julian Beever


Julian Beever


Julian Beever


Julian Beever


Julian Beever

Explanation

( ti )

real-world view

screen (retina)

focal point

drawn illusiondrawn illusion (equally perceived)

The Power of the Visional System

S th h i l t (HSV) i t f t b t it’So the human visual system (HSV) is not perfect, but it’s extremely powerful

Vision is an integral part of life

Vision is the gateway to higher-level regions of the brain

Exploit this fast and powerful processor for• complex data analyses, creative tasks, communicating ideas

The science of visualization

cse 564: visualization introduction - stony brook...

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