Geography 360 Principles of Cartography. April 24, 2006

Similar documents
EnvSci 360 Computer and Analytical Cartography

Color and Color Model. Chap. 12 Intro. to Computer Graphics, Spring 2009, Y. G. Shin

Color, Vision, & Perception. Outline

Introduction. The Spectral Basis for Color

Colors in Images & Video

CS 565 Computer Vision. Nazar Khan PUCIT Lecture 4: Colour

COLOR and the human response to light

excite the cones in the same way.

Understand brightness, intensity, eye characteristics, and gamma correction, halftone technology, Understand general usage of color

Color Science. What light is. Measuring light. CS 4620 Lecture 15. Salient property is the spectral power distribution (SPD)

Victor Ostromoukhov Université de Montréal. Victor Ostromoukhov - Université de Montréal

check it out online at

COLOR AS A DESIGN ELEMENT

COLOR. and the human response to light

LECTURE 07 COLORS IN IMAGES & VIDEO

Comp/Phys/Apsc 715. Example Videos. Administrative 1/23/2014. Lecture 5: Trichromacy, Color Spaces, Properties of Color

Additive. Subtractive

What is Color. Color is a fundamental attribute of human visual perception.

Image and video processing (EBU723U) Colour Images. Dr. Yi-Zhe Song

Additive Color Synthesis

Reading. Foley, Computer graphics, Chapter 13. Optional. Color. Brian Wandell. Foundations of Vision. Sinauer Associates, Sunderland, MA 1995.

Chapter 3 Part 2 Color image processing

Color. Color. Colorfull world IFT3350. Victor Ostromoukhov Université de Montréal. Victor Ostromoukhov - Université de Montréal

EECS490: Digital Image Processing. Lecture #12

Color Image Processing. Gonzales & Woods: Chapter 6

Myth #1. Blue, cyan, green, yellow, red, and magenta are seen in the rainbow.

Color Perception and Applications. Penny Rheingans University of Maryland Baltimore County. Overview

Christoph Wagner Colour Theory

Slide 1. Slide 2. Slide 3. Light and Colour. Sir Isaac Newton The Founder of Colour Science

To discuss. Color Science Color Models in image. Computer Graphics 2

The basic tenets of DESIGN can be grouped into three categories: The Practice, The Principles, The Elements

INSTITUTIONEN FÖR SYSTEMTEKNIK LULEÅ TEKNISKA UNIVERSITET

Digital Image Processing. Lecture # 8 Color Processing

Color & Graphics. Color & Vision. The complete display system is: We'll talk about: Model Frame Buffer Screen Eye Brain

Color Theory. Additive Color

Multimedia Systems Color Space Mahdi Amiri March 2012 Sharif University of Technology

CSE 332/564: Visualization. Fundamentals of Color. Perception of Light Intensity. Computer Science Department Stony Brook University

AP PSYCH Unit 4.2 Vision 1. How does the eye transform light energy into neural messages? 2. How does the brain process visual information? 3.

Adapted from the Slides by Dr. Mike Bailey at Oregon State University

Chapter Objectives. Color Management. Color Management. Chapter Objectives 1/27/12. Beyond Design

Overview of Human Cognition and its Impact on User Interface Design (Part 2)

Color and Perception. CS535 Fall Daniel G. Aliaga Department of Computer Science Purdue University

Color Perception. Color, What is It Good For? G Perception October 5, 2009 Maloney. perceptual organization. perceptual organization

CIE tri-stimulus experiment. Color Value Functions. CIE 1931 Standard. Color. Diagram. Color light intensity for visual color match

CS 544 Human Abilities

NEWTONIAN COLOR THEORY

Light. intensity wavelength. Light is electromagnetic waves Laser is light that contains only a narrow spectrum of frequencies

The Principles of Chromatics

Color theory Quick guide for graphic artists

Light and Colour. Light as part of the EM spectrum. Light as part of the EM spectrum

The Elements of Art: Photography Edition. Directions: Copy the notes in red. The notes in blue are art terms for the back of your handout.

Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester

Color Theory: Defining Brown

Color Science. CS 4620 Lecture 15

Introduction to Color Theory

H30: Specification of Colour, Munsell and NCS

III: Vision. Objectives:

Lecture Color Image Processing. by Shahid Farid

COLOR. Elements of color. Visible spectrum. The Fovea. Lecture 3 October 30, Ingela Nyström 1. There are three types of cones, S, M and L

VC 16/17 TP4 Colour and Noise

Figure 1: Energy Distributions for light

The human visual system

Visual Perception. human perception display devices. CS Visual Perception

Bettina Selig. Centre for Image Analysis. Swedish University of Agricultural Sciences Uppsala University

Color. Bilkent University. CS554 Computer Vision Pinar Duygulu

Mahdi Amiri. March Sharif University of Technology

Colour + Perception. CMPT 467/767 Visualization Torsten Möller. Pfister/Möller

For a long time I limited myself to one color as a form of discipline. Pablo Picasso. Color Image Processing

Color Perception. This lecture is (mostly) thanks to Penny Rheingans at the University of Maryland, Baltimore County

Objective Explain design concepts used to create digital graphics.

Digital Image Processing COSC 6380/4393. Lecture 20 Oct 25 th, 2018 Pranav Mantini

color basics theory & application Fall 2013 Ahmed Ansari Communication Design Fundamentals

Visual Perception. Overview. The Eye. Information Processing by Human Observer

the eye Light is electromagnetic radiation. The different wavelengths of the (to humans) visible part of the spectra make up the colors.

UBT128X Colour theory

12 Color Models and Color Applications. Chapter 12. Color Models and Color Applications. Department of Computer Science and Engineering 12-1

Color Appearance, Color Order, & Other Color Systems

Color Image Processing

Digital Image Processing

Fig Color spectrum seen by passing white light through a prism.

Value. Value-It is the lightness or darkness of an object, regardless of color. Value is relative to the background color and other items on the page.

Color Image Processing

Visual Perception. Jeff Avery

Chapter 9: Color. What is Color? Wavelength is a property of an electromagnetic wave in the frequency range we call light

Digital Image Processing Color Models &Processing

COLOR Cartographic Design & Principles Winter 2016

Human Vision, Color and Basic Image Processing

Understanding Color Theory Excerpt from Fundamental Photoshop by Adele Droblas Greenberg and Seth Greenberg

Raster Graphics. Overview קורס גרפיקה ממוחשבת 2008 סמסטר ב' What is an image? What is an image? Image Acquisition. Image display 5/19/2008.

קורס גרפיקה ממוחשבת 2008 סמסטר ב' Raster Graphics 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור

Color. Fredo Durand Many slides by Victor Ostromoukhov. Color Vision 1

Wireless Communication

Color. Chapter 6. (colour) Digital Multimedia, 2nd edition

Lecture 8. Color Image Processing

6 Color Image Processing

COLOR. Elements of color. Visible spectrum. The Human Visual System. The Fovea. There are three types of cones, S, M and L. r( λ)

GRAPHICS TECHNOLOGY II

IFT3355: Infographie Couleur. Victor Ostromoukhov, Pierre Poulin Dép. I.R.O. Université de Montréal

Digital Image Processing. Lecture # 6 Corner Detection & Color Processing

Color images C1 C2 C3

Transcription:

Geography 360 Principles of Cartography April 24, 2006

Outlines 1. Principles of color Color as physical phenomenon Color as physiological phenomenon 2. How is color specified? (color model) Hardware-oriented User-oriented 3. How do we effectively use color in map design?

1. Principles of color Color as physical phenomenon Electromagnetic energy Spectral reflectance curve Color as physiological phenomenon Human vision Theories of color perception

Color as physical phenomenon Perceiving color requires three elements: light source, object, and eye-brain system of the viewer When light strikes the map some of that light is reflected back to the eye of the map reader Color is the physical phenomenon of light, perceived by human eyes, associated with various wavelengths in the electromagnetic spectrum

Different color hues have difference spectral reflectance curves across wavelength Spectral reflectance curves Electromagnetic energy See Figure 10.2

Color as physiological phenomenon Theories of color perception Trichromatic theory Three kinds of cones (in retina) sensitive to particular wavelengths: short (blue), medium (green), and long (red) (see Figure 10.3, 10.5) Color perception is a function of the relative stimulation of the three types of cones Opponent-process theory Color perception is based on a lightness-darkness channel and two opponent color channels: red-green and blue-yellow (see Figure 10.6) We do not perceive mixtures of red and green or blue and yellow, but rather red and blue or red and yellow

Simultaneous contrast See Figure 10.7 The appearance of any color in a display depends on the colors that surround it This is an artifact of the way our brain interprets color, thus cannot be photographed Apparent color of an area will tend to shift toward the opponent color of the surrounding color When the surround is green, the gray tone appears reddish; in contrast, when the surround is blue, the gray tone appears yellowish Physiological limitations of human vision Any implication for map design?

2. How is color specified? Hardware-oriented How color is produced RGB model CMYK model User-oriented How color is perceived HSV model Munsell model

How is color produced? See color plate 10.1 Color additive process Operative when lights of different colors are superimposed Soft-copy color maps Color subtractive process Operative when dyes of different colors are superimposed Hard-copy color maps

The RGB model Colors are specified based on the intensity of red, green, and blue color guns See Figure 10.16 (p. 192) Related to the method of softcopy color production (see Figure 10.10 and 10.15: computer monitors use RGB guns or subpixels) Common notions of hue, saturation, and lightness are not inherent to the model Equal steps in the RGB color space do not correspond to equal visual steps

The CMYK model Colors are specified based on the portion of cyan, magenta, yellow, and black dyes Related to hardcopy color production Shares the same limitation as the RGB model (i.e. lack of direct linkage to human perception of color)

How color is perceived Psychological dimensions of color Desert island experiment: let s classify the pebbles by color green, yellow, gray, white, dark blue, light blue, brilliant blue, dusky blue Human perception of color consists of hue, saturation (chroma), and value (lightness) Hue: names for psychological experiences of particular electromagnetic wavelengths Saturation: addition of a neutral gray to a hue Value: addition of white or black to a hue

The HSV model Figure 10.17 shows how HSV is organized Hue at the base of the hexcone Saturation changes occur as you move from the center (lowest; grayscale) to the edge of the cone (highest; most saturated; pure) Value changes occur as you move from the base of the cone (highest; white) to the apex of the cone (lowest; black) along the vertical axis Color perception does not take a symmetric form as suggested by the HSV model (e.g. is the value of the lightest possible green equal to that of the lightest possible red as suggested by the model?) In other words, the HSV does not adopt perceptual scaling

The Munsell model See Color plate 10.3, Figure 10.18, 10.19 Similar to the HSV model in that it consists of hue, saturation, and value Different from the HSV model in that it is asymmetrical (because it is perceptual based)

The Munsell model is perceptual based

The Munsell model Dimen sion Scaling Figure Hue Plane of color wheel 10 letter designation 10 number division within each letter designation (e.g. 1R, 5G) Figure 10.19 Notation: H V/C 5R 5/14 Value Vertical axis 0 to 10 (darkest to lightest) upward Figure 10.18 Saturation (Chroma) Horizontal axis 0 to 16 (lest to most saturated) outward Figure 10.18

3. Color use in map design Color that reflects intellectual hierarchy Color that reflects data measurement Color that makes the use of its symbolic connotations and conventions Color that takes into account human limitations of color perception Color that takes into account preferences, age, and vision impairment

Color that reflects intellectual hierarchy Organize map elements hierarchically by visually rendering relative importance of map elements Choose a set of distinctive colors corresponding to map elements with varying intellectual hierarchies The distinctive color will be distant from other colors along three dimensions of color models Good use of color for enhancing visual hierarchy Most saturated vs. least saturated (blue and white) Poor use of color for enhancing visual hierarchy Similar hue with the same level of saturation (pink and red)

Color for visual hierarchy Using the same color hue for figure and ground does not render varying intellectual hierarchy Using the different color hue for figure and ground renders varying intellectual hierarchy where the color for figure is visually more dominant Using the different color value for figure and ground helps map readers separate figure from ground

Color for visual hierarchy Using the different color saturation for figure and ground helps map readers separate figure from ground See table 15.3 in Dent (p. 299)

Color for visual hierarchy Some hues look pure, while other hues look like mixtures; Consider the purity of hues when combining colors on a map to imply distinctive differences It is easier to see the colors of map elements when the map background is monochromatic (one hue) than mixtures (such as brown)

Color that reflects data measurement Appropriate color scheme should reflect data measurement Qualitative schemes: different hue can be used to trigger nominal differences Quantitative schemes: same hue with different value or saturation, or similar hues with different value or saturation Sequential scheme for unipolar data Diverging scheme for bipolar data

Color for data measurement - qualitative scheme - Hue does not suggest order, thus appropriate to render nominal differences If nominal differences carry the same weight, use distinct hues with similar value and saturation Image source: Krygier and Wood 2004

Color for data measurement - quantitative scheme - Sequential Diverging Value or saturation suggest order, thus appropriate to render progression in magnitudes If data has a natural dividing point, use diverging scheme

Color scheme for balanced data

Color for data measurement - multivariate data - Combine two or more variables where each variable is represented by hue, progression in magnitude within each variables is represented by value or saturation Image source: Brewer s color guideline

Color connotations We commonly associate hues with different physical phenomena, sensations, and emotions Landscape metaphor Blue for wetness and coldness, Tan for dryness, Green for lushness, Red for warmth Color relates to concepts Red for communism, blue for blue-collar occupation Map borrows from familiar concepts Traffic light color used for hazard mapping Color can reinforce the meaning of pictorial symbols Yellow dollar sign, symmetric red crosses on ambulances, vertical black crosses for cemeteries or churches

Color conventions Some color schemes follow conventions such as hypsometric tint or spectral color scheme (color plate 14.1) Color conventions do not necessarily conform to principles of map symbolization Color conventions do not necessarily coincide with color connotations

Color & human limitation The same hue may appear differently if surrounded by different colors For example, choose color pairs that are not affected by simultaneous contrast for diverging scheme because they should not be confused (see the 4 th column of Table 13.2)

Other considerations Preferences for color Apart from logics of map design, a particular map design can be preferred due to individual differences in color preference Also consider cultural differences Age Older map viewers have difficulty seeing colors and need more saturated colors Kids are not familiar with color conventions for map Color-blind map viewers In the U.S., 3% of females and 8% of males are color-blind, not distinguishing between reds and greens Consider using reds and blues or greens and blues instead (see the third column of Table 13.2)

Functions of color in map Structure: color can organize map elements, and structure the message communicated Visual hierarchy, data measurement Legibility: can enhance clarity Visual contrast, human limitations of color perception Overtone: can elicit subjective reactions to the map Color connotations