I590 Interactive Visual Analytics Week 4 Gestalt Laws Color - - PowerPoint PPT Presentation

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I590 Interactive Visual Analytics Week 4 Gestalt Laws Color - - PowerPoint PPT Presentation

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I590 Interactive Visual Analytics Week 4 Gestalt Laws Color Perception Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI Homework 0 Avg: 9.16 Min: 7 Max: 10 Last week Visual percep7on, edge detec7on, visual illusions, and

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Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI

Week 4 Gestalt Laws Color Perception

I590 Interactive Visual Analytics

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Homework 0

Avg: 9.16 Min: 7 Max: 10

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Last week

Visual percep7on, edge detec7on, visual illusions, and popouts Our visual system see differences, not absolute values, and is aIracted to edges. We can easily see objects that are different in color and shape, or that are in mo5on (popouts) Use color and shape sparingly to make the important informa5on pop out

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This week

More on visual percep7on:

  • Gestalt principles
  • Color percep5on
  • Color spaces
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Gestalt grouping principles

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Gestalt grouping principles

“The whole is other than the sum of its parts”

Our brain has innate capacity to see paIerns that transcend the visual s5muli the produce them

gestalt: form in German

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proximity

Andy Rutledge, “Gestalt Principles of Perception”

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proximity

Alex Lex

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proximity

  • B. Wong, “Gestalt Principles, I”
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similarity

  • B. Wong, “Gestalt Principles, I”
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similarity

Andy Rutledge, “Gestalt Principles of Perception”

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connectedness

Ware, “Information Visualization”

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link surface

  • utline

enclosure

Alex Lex

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A li=le experiment…

Based on a slide by Alex Lex

How many groups do you see?

proximity color similarity size similarity shape similarity

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A li=le experiment…

How many groups do you see?

proximity color similarity size similarity shape similarity

Based on a slide by Alex Lex

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A li=le experiment…

How many groups do you see?

proximity color similarity size similarity shape similarity

Based on a slide by Alex Lex

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grouping

  • B. Wong, “Gestalt Principles, I”
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grouping

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grouping

Jorge Camoes Via Miriah Meyer

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grouping - common fate

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closure

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closure

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figure / background

Mariah Meyer

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Gestalt principles

similarity: objects that look like each other (in size, color, or shape) are related proximity: objects that are visually close to each

  • ther are related

connections: objects that are visually connected are related closure: we see incomplete shapes as complete figure / ground: elements are perceived as either figures or background common fate: elements with the same moving direc5on are perceived as a unit

Mariah Meyer

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color

The Huffington Post

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color

the property possessed by an object of producing different sensa5ons on the eye as a result of the way the

  • bject reflects or emits light
  • Oxford dictionary
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Electromagne5c radia5on within a certain range [400nm

  • 700nm] of the electromagne5c spectrum

more energy

light

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Mariah Meyer

light

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Trichromacy

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Trichromacy

Normal human color vision is 3 dimensional Derived from three cone types (short, medium, and large wave-length sensi5vity) Each type of cone contains a specific photosensi5ve pigment that reacts to a certain wavelength of light

Based on a slide by Mariah Meyer

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Trichromacy

difficult to read difficult to read easy to read easy to read

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Explains how signals are processed

Opponent-process theory

Visual perceptual system detects differences in the response of cones

+

luminance

  • red-green
  • pponent channel

+

yellow-blue

  • pponent channel
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  • C. Ware, Visual Thinking for Design

Sensitivity to spatial detail

The luminance channel has greater ability to resolve smaller detail

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  • C. Ware, Visual Thinking for Design

Sensitivity to spatial detail

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“Important” colors

These colors have a name in virtually every human language Their seman5cs and connota5ons are culture- specific

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hue saturation luminance

Conventions

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Color deficiencies

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Color deficiencies

Some5mes caused by faculty cones, some5mes by faulty pathways red-green weakness is the most common type 8% of (North American) makes, 0.5% of female Can be explained by opponent color theory

Based on a slide by Miriah Meyer

normal re5na Protanopic

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Via Miriah Meyer

lacking green cones lacking red cones lacking blue cones

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difficult to dis5nguish for people with Deuteranopia

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Design critique

http://tinyurl.com/gueqapz

  • What is the visualiza5on

about?

  • What data is represented in

the visualiza5on? And how?

  • What are the interac5ons

used?

  • What ques5ons can we

answer with the visualiza5on?

  • Do you like the visualiza5on?
  • Are there any improvements

that can be made to the design?

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Color spaces

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light

  • 1. pure yellow: 580 nm
  • 2. color matching

yellow

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red green blue

Tristimulus color matching

test color

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red green blue

Tristimulus color matching

test color 580nm

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Tristimulus color matching

red green blue

0.17 0.17

test color 580nm

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RGB color space

Each point within the cube is defined by a 3D vector (r, g,b) and represents a unique color The r, g, b coordinates of the vector reflect a combina5on of red, green, and blue primaries needed to reproduce the color

1.0 1.0 1.0

Each point within the cube is defined by a 3D vector (r, g,b) and represents a unique color The r, g, b coordinates of the vector reflect a combina5on of red, green, and blue primaries needed to reproduce the color G B R

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1.0 1.0 1.0

yellow (1.0, 1.0, 0.0)

RGB color space

Each point within the cube is defined by a 3D vector (r, g,b) and represents a unique color The r, g, b coordinates of the vector reflect a combina5on of red, green, and blue primaries needed to reproduce the color

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RGB color space

1.0 1.0 1.0

Each point within the cube is defined by a 3D vector (r, g,b) and represents a unique color The r, g, b coordinates of the vector reflect a combina5on of red, green, and blue primaries needed to reproduce the color

(1.0, 0.6, 1.4)

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1.0 1.0 1.0

magenta (1.0, 0.0, 1.0)

RGB color space

1.0 1.0 1.0

Each point within the cube is defined by a 3D vector (r, g,b) and represents a unique color The r, g, b coordinates of the vector reflect a combina5on of red, green, and blue primaries needed to reproduce the color

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1.0 1.0 1.0

white (1.0, 1.0, 1.0)

RGB color space

1.0 1.0 1.0

Each point within the cube is defined by a 3D vector (r, g,b) and represents a unique color The r, g, b coordinates of the vector reflect a combina5on of red, green, and blue primaries needed to reproduce the color

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1.0 1.0 1.0

black (0.0, 0.0, 0.0)

RGB color space

1.0 1.0 1.0

Each point within the cube is defined by a 3D vector (r, g,b) and represents a unique color The r, g, b coordinates of the vector reflect a combina5on of red, green, and blue primaries needed to reproduce the color

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RGB color space

G B R

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what colors combina7on can be used to re- producing the visible light spectrum by mixing?

  • red, yellow, blue
  • red, green, blue
  • range, green, violet
  • cyan, magenta, yellow
  • all of the above

Miriah Meyer

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Light mixing (RGB)

Addi5ve mixing of colored lights

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Light mixing (RGB)

LCD display closeup

Wikipedia

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Ink mixing (CMY / CMYK)

Subtrac5ve mixing of inks printed on white paper

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Color picture CMY composite

Wikipedia

CMYK composite

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  • red, yellow, blue
  • red, green, blue
  • range, green, violet
  • cyan, magenta, yellow
  • all of the above

Miriah Meyer

, almost

what colors combina7on can be used to re- producing the visible light spectrum by mixing?

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red green blue test color 500nm

Tristimulus color matching

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green blue test color 500nm

Tristimulus color matching

red

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red green blue test color 500nm

Tristimulus color matching

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green blue test color 500nm red

Tristimulus color matching

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Opps

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CIE color space

  • At a mee5ng in of the CIE in 1931
  • Let’s have imaginary primary colors!
  • Construct linear, possibly non-realizable combina5ons of

primaries so that color matching func5ons are posi5ve throughout the visible light

  • X, Y, Z primaries
  • Can be linearly transformed from RGB (and vice versa)

Based on a slide by Siddhartha Chaudhuri

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CIE color space

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Y Z X

1.0 1.0 1.0

Y Z X

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Y Z X

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CIE chromaticity diagram

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CIE chromaticity diagram

White

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CIE chromaticity diagram

R G Y

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CIE chromaticity diagram

White

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CIE chromaticity diagram

RGB color space

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CIE chromaticity diagram

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Perceptual color spaces

A change in the amount of color value should produce a propor5onal change in the way we see the color

Via Miriah Meyer

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HSL

Rearrangement of the RGB color space into a cylinder to be more intui5ve and perceptually relevant

  • hue: what people think of as

color

  • saturation: the vividness of

the color

  • luminance: amount of black

mixed in

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hue saturation luminance

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Guidelines for using color in visualization

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Colormap

Specifies a mapping between color and values

[0, 8] categorical vs.

  • rdered

sequential vs. diverging discrete vs. continuous

Match colormap to data type & task

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distinguishability

Via Miriah Meyer

we can iden5fy/remember about 6-12 unique colors

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Order these colors…

Miriah Meyer

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Order these colors…

Miriah Meyer

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Order these colors…

Miriah Meyer

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Ordered colormaps should vary along satura5on and luminance Hue is good for categorical data Categorical colors are easier to remember if they are nameable

guidelines

Colin Ware

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the rainbow colormap

temperature

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the rainbow colormap

  • rder?
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the rainbow colormap

sharp boundary

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the rainbow colormap

not color blind safe

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the rainbow colormap

Rainbow colormaps should be avoided as a default op5on for ordered data A safer, more effec5ve op5on is a colormap that varies in satura7on or

  • luminance. Ideally both
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Colin Ware

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Simultaneous contrast

Via Colin Ware

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Take home point

Knowing about percep5on is integral to designing good visualiza5ons (and user interfaces) With this informa5on, we can design visual encodings that communicate data accurately and draw the user’s aIen5on as needed

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Next week

Data abstrac5ons (chapter 2) Marks and channels: the visualiza5on alphabet (chapter 5)