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Berkeley COMPSCI C280 - Lecture 3: Color

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Slide 1ColorLast timeSlide 4Today: ColorColor and lightColor and lightElectromagnetic spectrumMeasuring spectraSpectral power distributionSlide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Color mixingAdditive color mixingExamples of additive color systemsSuperpositionSubtractive color mixingExamples of subtractive color systemsToday: ColorWhy specify color numerically?Color matching experimentsColor matching experimentsColor matching experimentsColor matching experiment 1Color matching experiment 1Color matching experiment 1Color matching experiment 1Color matching experiment 2Color matching experiment 2Color matching experiment 2Color matching experiment 2Color matchingMetamersGrassman’s lawsComputing color matchesSlide 41Slide 42Slide 43Slide 44Today: ColorStandard color spacesRGB color spaceCIE XYZ color spaceSlide 49HSV color spaceDistances in color spaceDistances in color spaceUniform color spacesToday: ColorColorHuman photoreceptorsSlide 57Human photoreceptorsHuman photoreceptorsTrichromacyEnvironmental effects & adaptationChromatic adaptationChromatic adaptationBrightness perceptionSlide 65Slide 66Slide 67Slide 68Slide 69Slide 70Slide 71Slide 72Slide 73After imagesName that colorYellow Text on a Blue BackgroundOther isoluminance effects“Two pathways”Today: ColorColor as a low-level cue for CBIRSlide 81Color as a low-level cue for CBIRColor-based image retrievalExample databaseExample retrievalsExample retrievalsColor-based skin detectionColor-based segmentation for robot soccerColor-based appearance models for body trackingViewing Colored ObjectsFinding SpecularitiesSkewed-TSkewed-TSlide 94Today: ColorNext timeC280, Computer VisionProf. Trevor [email protected] 3: Color`ColorColorReadings:–Forsyth and Ponce, Chapter 6–Szeliski, 2.3.2Last time•Image formation affected by geometry, photometry, and optics.•Projection equations express how world points mapped to 2d image.•Homogenous coordinates allow linear system for projection equations.•Lenses make pinhole model practical•Photometry models: Lambertian, BRDF•Digital imagers, Bayer demosaicingParameters (focal length, aperture, lens diameter, sensor sampling…) strongly affect image obtained.K. GraumanSlide Credits•Kristen Grauman: 3-48, 50-75, 79-86•Bob Woodham: 49, 87-90•and others, indirectly (Steve Palmer, Brian Wandell, etc!)Today: Color•Measuring color–Spectral power distributions–Color mixing–Color matching experiments–Color spaces•Uniform color spaces•Perception of color–Human photoreceptors–Environmental effects, adaptation•Using color in machine vision systemsColor and light•Color of light arriving at camera depends on–Spectral reflectance of the surface light is leaving–Spectral radiance of light falling on that patch•Color perceived depends on–Physics of light–Visual system receptors–Brain processing, environmentColor and lightNewton 1665Image from http://micro.magnet.fsu.edu/White light: composed of about equal energy in all wavelengths of the visible spectrumImage credit: nasa.govElectromagnetic spectrumHuman Luminance Sensitivity FunctionMeasuring spectraFoundations of Vision, B. WandellSpectroradiometer: separate input light into its different wavelengths, and measure the energy at each.400 500 600 700Wavelength (nm.)# Photons(per ms.)Spectral power distribution•The power per unit area at each wavelength of a radiant objectFigure © Stephen E. Palmer, 2002Spectral power distributions.# PhotonsD. Normal DaylightWavelength (nm.)B. Gallium Phosphide Crystal400 500 600 700# PhotonsWavelength (nm.)A. Ruby Laser400 500 600 700400 500 600 700# PhotonsC. Tungsten Lightbulb400 500 600 700# PhotonsSome examples of the spectra of light sources© Stephen E. Palmer, 2002Spectral reflectances for some natural objects: how much of each wavelength is reflected for that surfaceForsyth & Ponce, measurements by E. KoivistoThe color viewed is also affected by the surface’s spectral reflectance properties.Surface reflectance spectraSome examples of the reflectance spectra of surfacesWavelength (nm)% Photons ReflectedRed400 700Yellow400 700Blue400 700Purple400 700© Stephen E. Palmer, 2002The Psychophysical CorrespondenceThere is no simple functional description for the perceivedcolor of all lights under all viewing conditions, but …...A helpful constraint: Consider only physical spectra with normal distributionsareaWavelength (nm.)# Photons400 700500 600meanvariance© Stephen E. Palmer, 2002The Psychophysical CorrespondenceMean Hueyellowgreenblue# PhotonsWavelength© Stephen E. Palmer, 2002The Psychophysical CorrespondenceVariance SaturationWavelengthhighmediumlowhi.med.low# Photons© Stephen E. Palmer, 2002The Psychophysical CorrespondenceArea Brightness# PhotonsWavelengthB. Area Lightnessbrightdark© Stephen E. Palmer, 2002Color mixingSource: W. FreemanCartoon spectra for color names:Additive color mixingColors combine by adding color spectraLight adds to black.Source: W. FreemanExamples of additive color systemshttp://www.jegsworks.comhttp://www.crtprojectors.co.uk/CRT phosphorsmultiple projectorsSuperposition•Additive mixing: The spectral power distribution of the mixture is the sum of the spectral power distributions of the components.Figure from B. Wandell, 1996Subtractive color mixingColors combine by multiplying color spectra.Pigments remove color from incident light (white).Source: W. FreemanExamples of subtractive color systems•Printing on paper•Crayons•Most photographic filmToday: Color•Measuring color–Spectral power distributions–Color mixing–Color matching experiments–Color spaces•Uniform color spaces•Perception of color–Human photoreceptors–Environmental effects, adaptation•Using color in machine vision systemsWhy 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–11: black, blue, brown, grey, green, orange, pink, purple, red, white, and yellow.–Other languages have fewer/more.–Common to disagree on appropriate color names.•Color reproduction problems increased by prevalence of digital imaging – e.g. digital libraries of art. –How to ensure that everyone perceives the same color?–What spectral radiances produce the same response from people under simple


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Berkeley COMPSCI C280 - Lecture 3: Color

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