What is color? What do we mean by: Color of an object Color of a light Subjective color impression. Are all these notions the same?

Similar documents
Causes of color. Radiometry for color

Colour Reading: Chapter 6. Black body radiators

Why does a visual system need color? Color. Why does a visual system need color? (an incomplete list ) Lecture outline. Reading: Optional reading:

Color. Reading: Optional reading: Chapter 6, Forsyth & Ponce. Chapter 4 of Wandell, Foundations of Vision, Sinauer, 1995 has a good treatment of this.

Announcements. Lighting. Camera s sensor. HW1 has been posted See links on web page for readings on color. Intro Computer Vision.

Digital Image Processing COSC 6380/4393. Lecture 19 Mar 26 th, 2019 Pranav Mantini

Lecture 1 Image Formation.

Color. Computational Photography MIT Feb. 14, 2006 Bill Freeman and Fredo Durand

ECE-161C Color. Nuno Vasconcelos ECE Department, UCSD (with thanks to David Forsyth)

Announcements. Camera Calibration. Thin Lens: Image of Point. Limits for pinhole cameras. f O Z

Lighting. Camera s sensor. Lambertian Surface BRDF

CSE 167: Lecture #7: Color and Shading. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011

Color and Shading. Color. Shapiro and Stockman, Chapter 6. Color and Machine Vision. Color and Perception

1.1.1 Radiometry for Coloured Lights: Spectral Quantities

Color to Binary Vision. The assignment Irfanview: A good utility Two parts: More challenging (Extra Credit) Lighting.

Introduction to color science

CS5670: Computer Vision

The Elements of Colour

CMSC427 Shading Intro. Credit: slides from Dr. Zwicker

Optics Test Science What are some devices that you use in everyday life that require optics?

CS6670: Computer Vision

CS6670: Computer Vision

Illumination and Shading

Reading. 2. Color. Emission spectra. The radiant energy spectrum. Watt, Chapter 15.

CSE 167: Introduction to Computer Graphics Lecture #6: Colors. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2013

CS4670: Computer Vision

4. Refraction. glass, air, Perspex and water.

(0, 1, 1) (0, 1, 1) (0, 1, 0) What is light? What is color? Terminology

Chapter 5 Extraction of color and texture Comunicação Visual Interactiva. image labeled by cluster index

Today. Global illumination. Shading. Interactive applications. Rendering pipeline. Computergrafik. Shading Introduction Local shading models

Light. Properties of light. What is light? Today What is light? How do we measure it? How does light propagate? How does light interact with matter?

What is Color and How is It Measured?

All forms of EM waves travel at the speed of light in a vacuum = 3.00 x 10 8 m/s This speed is constant in air as well

Physics 1230: Light and Color

Color Vision. Spectral Distributions Various Light Sources

Digital Image Processing

Announcements. Rotation. Camera Calibration

SNC 2PI Optics Unit Review /95 Name:

this is processed giving us: perceived color that we actually experience and base judgments upon.

Announcements. Light. Properties of light. Light. Project status reports on Wednesday. Readings. Today. Readings Szeliski, 2.2, 2.3.

CS 556: Computer Vision. Lecture 18

Image Formation. Camera trial #1. Pinhole camera. What is an Image? Light and the EM spectrum The H.V.S. and Color Perception

Color and Light CSCI 4229/5229 Computer Graphics Fall 2016

Lecture 16 Color. October 20, 2016

Light Transport Baoquan Chen 2017

CSE 167: Introduction to Computer Graphics Lecture #6: Lights. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2016

MODELING LED LIGHTING COLOR EFFECTS IN MODERN OPTICAL ANALYSIS SOFTWARE LED Professional Magazine Webinar 10/27/2015

Lighting affects appearance

Today. Global illumination. Shading. Interactive applications. Rendering pipeline. Computergrafik. Shading Introduction Local shading models

Colour computer vision: fundamentals, applications and challenges. Dr. Ignacio Molina-Conde Depto. Tecnología Electrónica Univ.

Visualisatie BMT. Rendering. Arjan Kok

Rules for Deviation of Light Rays During Refraction

The exam begins at 2:40pm and ends at 4:00pm. You must turn your exam in when time is announced or risk not having it accepted.

Sources, shadows and shading

Mech 296: Vision for Robotic Applications

Unit 3: Optics Chapter 4

Light and Sound. Wave Behavior and Interactions

Digital Image Processing. Introduction

Virtual Reality for Human Computer Interaction

Color. Phillip Otto Runge ( )

Distributed Algorithms. Image and Video Processing

Starting this chapter

Computer Graphics. Bing-Yu Chen National Taiwan University The University of Tokyo

Lecture 12 Color model and color image processing

Color and Light. CSCI 4229/5229 Computer Graphics Summer 2008

Announcements. The principle of trichromacy. Color Matching Functions. The appearance of colors

Image Formation. Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and Media Arts University of New Mexico

Lecture #13. Point (pixel) transformations. Neighborhood processing. Color segmentation

Understanding Variability

1. Final Projects 2. Radiometry 3. Color. Outline

Module 3. Illumination Systems. Version 2 EE IIT, Kharagpur 1

Light and all its colours

Property of: Entrada Interactive. PBR Workflow. Working within a PBR-based environment

2/26/2016. Chapter 23 Ray Optics. Chapter 23 Preview. Chapter 23 Preview

Physical Color. Color Theory - Center for Graphics and Geometric Computing, Technion 2

When this experiment is performed, subjects find that they can always. test field. adjustable field

CSE 167: Lecture #6: Color. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011

4.5 Images Formed by the Refraction of Light

Fall 2015 Dr. Michael J. Reale

Conceptual Physics Fundamentals

Reading. 4. Color. Outline. The radiant energy spectrum. Suggested: w Watt (2 nd ed.), Chapter 14. Further reading:

Lighting affects appearance

Siggraph Course 2017 Path Tracing in Production Part 1 Manuka: Weta Digital's Spectral Renderer

Lecture 15: Shading-I. CITS3003 Graphics & Animation

Chapter 33 The Nature and Propagation of Light by C.-R. Hu

Introduction. Lighting model Light reflection model Local illumination model Reflectance model BRDF

CSE 167: Lecture #6: Color. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2012

Visible Color. 700 (red) 580 (yellow) 520 (green)

11.1 CHARACTERISTICS OF LIGHT

w Foley, Section16.1 Reading

CENG 477 Introduction to Computer Graphics. Ray Tracing: Shading

Sources, Surfaces, Eyes

CS 5625 Lec 2: Shading Models

Introduction to Computer Graphics with WebGL

10.4 Interference in Thin Films

Spectral Color and Radiometry

CG T8 Colour and Light

Global Illumination. Frank Dellaert Some slides by Jim Rehg, Philip Dutre

Hot Sync. Materials Needed Today

TracePro Stray Light Simulation

Transcription:

What is color? What do we mean by: Color of an object Color of a light Subjective color impression. Are all these notions the same? Wavelengths of light striking the eye are not sufficient or necessary for unique color impression. Surface color of an object is usually defined via its reflection function independent of any particular light s spectral distribution Color of a light is usually defined in terms of its spectral distribution. Sources of variations in the amount of light at different wavelengths: Light could be produced in different amounts at different wavelengths (compare the sun and a fluorescent light bulb). Light could be differentially reflected (e.g. some pigments). It could be differentially refracted - (e.g. Newton s prism) Wavelength dependent specular reflection - e.g. shiny copper penny (actually most metals). Florescence - light at invisible wavelengths is absorbed and reemitted at visible wavelengths.

Radiometry for colour All definitions are now per unit wavelength All units are now per unit wavelength All terms are now spectral Radiance becomes spectral radiance watts per square meter per steradian per unit wavelength

Defining Source Color: Black body radiators Construct a hot body with near-zero albedo (black body) Easiest way to do this is to build a hollow metal object with a tiny hole in it, and look at the hole. The spectral power distribution of light leaving this object is a simple function of temperature ( ) µ 1 l 5 E l This leads to the notion of color temperature --- the temperature of a black body that would look the same Ê Ë ˆ Ê Á Ë 1 ˆ exp( hc klt )-1

Violet Indigo Blue Green Yellow Orange Red Measurements of relative spectral power of sunlight, made by J. Parkkinen and P. Silfsten. Relative spectral power is plotted against wavelength in nm. The visible range is about 400nm to 700nm. The color names on the horizontal axis give the color names used for monochromatic light of the corresponding wavelength --- the colors of the rainbow.

Violet Indigo Blue Green Yellow Orange Red Relative spectral power of two standard illuminant models --- D65 models sunlight,and illuminant A models incandescent lamps. Relative spectral power is plotted against wavelength in nm. The visible range is about 400nm to 700nm. The color names on the horizontal axis give the color names used for monochromatic light of the corresponding wavelength --- the colors of the rainbow.

Measurements of relative spectral power of four different artificial illuminants, made by H.Sugiura. Relative spectral power is plotted against wavelength in nm. The visible range is about 400nm to 700nm.

Spectral albedoes for several different leaves, with color names attached. Notice that different colours typically have different spectral albedo, but that different spectral albedoes may result in the same perceived color (compare the two whites). Spectral albedoes are typically quite smooth functions. Measurements by E.Koivisto.

The appearance of colors Color appearance is strongly affected by (at least): other nearby colors, adaptation to previous views state of mind We show several demonstrations in what follows. Film color mode: View a colored surface through a hole in a sheet, so that the color looks like a film in space; controls for nearby colors, and state of mind. Other modes: Surface color Volume color Mirror color Illuminant color

The appearance of colors Hering, Helmholtz: Color appearance is strongly affected by other nearby colors, by adaptation to previous views, and by state of mind Film color mode: View a colored surface through a hole in a sheet, so that the color looks like a film in space; controls for nearby colors, and state of mind. Other modes: Surface colour Volume colour Mirror colour Illuminant colour By experience, it is possible to match almost all colors, viewed in film mode using only three primary sources - the principle of trichromacy. Other modes may have more dimensions Glossy-matte Rough-smooth Most of what follows discusses film mode.

Munker-White Illusion

Simutaneous Contrast Illusion

Paint or Shading?

Why specify color numerically? Accurate color reproduction is commercially valuable Many products are identified by color ( golden arches; Few color names are widely recognized by English speakers - About 10; other languages have fewer/more, but not many more. It s common to disagree on appropriate color names. Color reproduction problems increased by prevalence of digital imaging - eg. digital libraries of art. How do we ensure that everyone sees the same color?

Color matching experiments - I Show a split field to subjects; one side shows the light whose color one wants to measure, the other a weighted mixture of primaries (fixed lights). Each light is seen in film color mode.

Color matching experiments - II Many colors can be represented as a mixture of A, B, C write M=a A + b B + c C where the = sign should be read as matches This is additive matching. Gives a color description system - two people who agree on A, B, C need only supply (a, b, c) to describe a color.

Subtractive matching Some colors can t be matched like this: instead, must write This is subtractive matching. Interpret this as (-a, b, c) M+a A = b B+c C Problem for building monitors: Choose R, G, B such that positive linear combinations match a large set of colors

The principle of trichromacy Experimental facts: Three primaries will work for most people if we allow subtractive matching Exceptional people can match with two or only one primary. This could be caused by a variety of deficiencies. Most people make the same matches. There are some anomalous trichromats, who use three primaries but make different combinations to match These matches can be used to determine the genetics of their photopigments.

Grassman s Laws For colour matches made in film colour mode: symmetry: U=V <=>V=U transitivity: U=V and V=W => U=W proportionality: U=V <=> tu=tv additivity: if any two (or more) of the statements U=V, W=X, (U+W)=(V+X) are true, then so is the third These statements are as true as any biological law. They mean that color matching in film color mode is linear.

Linear color spaces A choice of primaries yields a linear color space --- the coordinates of a color are given by the weights of the primaries used to match it. Choice of primaries is equivalent to choice of color space. RGB: primaries are monochromatic energies are 645.2nm, 526.3nm, 444.4nm. CIE XYZ: Primaries are imaginary, but have other convenient properties. Color coordinates are (X,Y,Z), where X is the amount of the X primary, etc. Usually draw x, y, where x=x/(x+y+z) y=y/(x+y+z)

Color matching functions Choose primaries, say A, B, C Given energy function, E(l) what amounts of primaries will match it? For each wavelength, determine how much of A, of B, and of C is needed to match light of that wavelength alone. a(l) b(l) c(l) Then our match is: { Ú a(l)e(l)dl }A + { Ú b(l)e(l)dl }B + { Ú c(l)e(l)dl }C These are colormatching functions

RGB: primaries are monochromatic, energies are 645.2nm, 526.3nm, 444.4nm. Color matching functions have negative parts -> some colors can be matched only subtractively.

CIE XYZ: Color matching functions are positive everywhere, but primaries are imaginary. Usually draw x, y, where x=x/(x+y+z) y=y/(x+y+z)

Color receptors Principle of univariance: cones give the same kind of response, in different amounts, to different wavelengths. The output of the cone is obtained by summing over wavelengths. Responses are measured in a variety of ways (comparing behaviour of color normal and color deficient subjects). All experimental evidence suggests that the response of the k th type of cone can be written as r Ú k (l)e(l)dl r k (l) where is the sensitivity of the receptor and spectral energy density of the incoming light.

Color receptors Relative fraction light absorbed S M L l Plot shows relative sensitivity as a function of wavelength, for the three cones. The S (for short) cone responds most strongly at short wavelengths; the M (for medium) at medium wavelengths and the L (for long) at long wavelengths. These are occasionally called B, G and R cones respectively, but that s misleading - you don t see red because your R cone is activated.

Simpler if we discretize frequency E(l) l

What does color matching do? È f 1 È r a T Í Í r r Choose Í f 2 = Í b T E r Î Í f 3 Í Î c T È I r È T r r Í Í T r Such that when : Í I g = Í r g E T Î Í I b Î Í r b Then the combined primaries yields: È I r È T r r Í Í T r r Í I g = Í r g A B T Î Í I b Î Í r b r C È f 1 [ ] Í f 2 Í Î Í f 3

A qualitative rendering of the CIE (x,y) space. The blobby region represents visible colors. There are sets of (x, y) coordinates that don t represent real colors, because the primaries are not real lights (so that the color matching functions could be positive everywhere).

A plot of the CIE (x,y) space. We show the spectral locus (the colors of monochromatic lights) and the blackbody locus (the colors of heated black-bodies). I have also plotted the range of typical incandescent lighting.

Non-linear colour spaces HSV: Hue, Saturation, Value are non-linear functions of XYZ. because hue relations are naturally expressed in a circle Uniform: equal (small!) steps give the same perceived color changes. Munsell: describes surfaces, rather than lights - less relevant for graphics. Surfaces must be viewed under fixed comparison light

HSV hexcone

Uniform color spaces McAdam ellipses (next slide) demonstrate that differences in x,y are a poor guide to differences in color Construct color spaces so that differences in coordinates are a good guide to differences in color.

Variations in color matches on a CIE x, y space. At the center of the ellipse is the color of a test light; the size of the ellipse represents the scatter of lights that the human observers tested would match to the test color; the boundary shows where the just noticeable difference is. The ellipses on the left have been magnified 10x for clarity; on the right they are plotted to scale. The ellipses are known as MacAdam ellipses after their inventor. The ellipses at the top are larger than those at the bottom of the figure, and that they rotate as they move up. This means that the magnitude of the difference in x, y coordinates is a poor guide to the difference in color.

CIE u v which is a projective transform of x, y. We transform x,y so that ellipses are most like one another. Figure shows the transformed ellipses.

Color receptors and color deficiency Trichromacy is justified - in color normal people, there are three types of color receptor, called cones, which vary in their sensitivity to light at different wavelengths (shown by molecular biologists). Deficiency can be caused by CNS, by optical problems in the eye, or by absent receptor types Usually a result of absent genes. Some people have fewer than three types of receptor; most common deficiency is red-green color blindness in men. Color deficiency is less common in women; red and green receptor genes are carried on the X chromosome, and these are the ones that typically go wrong. Women need two bad X chromosomes to have a deficiency, and this is less likely.

Adaptation phenomena The response of your color system depends both on spatial contrast and what it has seen before (adaptation) This seems to be a result of coding constraints --- receptors appear to have an operating point that varies slowly over time, and to signal some sort of offset. One form of adaptation involves changing this operating point. Common example: walk inside from a bright day; everything looks dark for a bit, then takes its conventional brightness.

Viewing coloured objects Assume diffuse+specular model Specular specularities on dielectric objects take the colour of the light specularities on metals can be coloured Diffuse colour of reflected light depends on both illuminant and surface people are surprisingly good at disentangling these effects in practice (colour constancy) this is probably where some of the spatial phenomena in colour perception come from

When one views a colored surface, the spectral radiance of the light reaching the eye depends on both the spectral radiance of the illuminant, and on the spectral albedo of the surface. We re assuming that camera receptors are linear, like the receptors in the eye. This is usually the case.

Subtractive mixing of inks Inks subtract light from white, whereas phosphors glow. Linearity depends on pigment properties inks, paints, often hugely nonlinear. Inks: Cyan=White-Red, Magenta=White-Green, Yellow=White-Blue. For a good choice of inks, and good registration, matching is linear and easy eg. C+M+Y=White- White=Black C+M=White-Yellow=Blue Usually require CMY and Black, because colored inks are more expensive, and registration is hard For good choice of inks, there is a linear transform between XYZ and CMY

Finding Specularities Assume we are dealing with dielectrics specularly reflected light is the same color as the source Reflected light has two components diffuse specular and we see a weighted sum of these two Specularities produce a characteristic dogleg in the histogram of receptor responses in a patch of diffuse surface, we see a color multiplied by different scaling constants (surface orientation) in the specular patch, a new color is added; a dog-leg results

B S Illuminant color T G Diffuse component R

B Diffuse region Boundary of specularity B G G R R