DOCSLIB.ORG

Color Appearance Models Second Edition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Color Appearance Models Second Edition Color Appearance Models Second Edition Mark D. Fairchild Munsell Color Science Laboratoiy Rochester Institute of Technology, USA John Wiley & Sons, Ltd Contents Series Preface xiii Preface XV Introduction xix 1 Human Color Vision 1 1.1 Optics of the Eye 1 1.2 The Retina 6 1.3 Visual Signal Processing 12 1.4 Mechanisms of Color Vision 17 1.5 Spatial and Temporal Properties of Color Vision 26 1.6 Color Vision Deficiencies 30 1.7 Key Features for Color Appearance Modeling 34 2 Psychophysics 35 2.1 Psychophysics Defined 36 2.2 Historical Context 37 2.3 Hierarchy of Scales 40 2.4 Threshold Techniques 42 2.5 Matching Techniques 45 2.6 One-Dimensional Scaling 46 2.7 Multidimensional Scaling 49 2.8 Design of Psychophysical Experiments 50 2.9 Importance in Color Appearance Modeling 52 3 Colorimetry 53 3.1 Basic and Advanced Colorimetry 53 3.2 Whyis Color? 54 3.3 Light Sources and Illuminants 55 3.4 Colored Materials 59 3.5 The Human Visual Response 66 3.6 Tristimulus Values and Color Matching Functions 70 3.7 Chromaticity Diagrams 77 3.8 CIE Color Spaces 78 3.9 Color Difference Specification 80 3.10 The Next Step , 82 viii CONTENTS 4 Color Appearance Terminology 83 4.1 Importance of Definitions 83 4.2 Color 84 4.3 Hue 85 4.4 Brightness and Lightness 86 4.5 Colorfulness and Chroma 87 4.6 Saturation 88 4.7 Unrelated and Related Colors 88 4.8 Definitions in Equations 90 4.9 Brightness-Colorfulness vs Lightness-Chroma 91 5 Color Order Systems 94 5.1 Overvlew and Requirements 94 5.2 The Munsell Book of Color 96 5.3 The Swedish Natural Color System (NCS) 99 5.4 The Colorcurve System 102 5.5 Other Color Order Systems 103 5.6 Uses of Color Order Systems 106 5.7 Color Naming Systems 109 6 Color Appearance Phenomena 111 6.1 What Are Color Appearance Phenomena? 111 6.2 Simultaneous Contrast, Crispening, and Spreading 113 6.3 Bezold-Brücke Hue Shift (Hue Changes with Luminance) 116 6.4 Abney Effect (Hue Changes with Colorimetric Purity) 117 6.5 Helmholtz-Kohlrausch Effect (Brightness Depends on Luminance and Chromaticity) 119 6.6 Hunt Effect (Colorfulness Increases with Luminance) 120 6.7 Stevens Effect (Contrast Increases with Luminance) 122 6.8 Helson- Judd Effect (Hue of Nonselective Samples) 123 6.9 Bartleson-Breneman Equations (Image Contrast Changes with Surround) 125 6.10 Discounting the Illuminant 127 6.11 Other Context and Structural Effects 127 6.12 Color Constancy? 132 7 Viewing Conditions 134 7.1 Configuration of the Viewing Field 134 7.2 Colorimetric Specification of the Viewing Field 138 7.3 Modes of Viewing 141 7.4 Unrelated and Related Colors Revisited 144 8 Chromatic Adaptation 146 8.1 Light, Dark, and Chromatic Adaptation 147 8.2 Physiology 149 8.3 Sensory and Cognitive Mechanisms 157 CONTENTS • 8.4 Corresponding-colors Data 159 8.5 Models 162 8.6 Computational Color Constancy 164 9 Chromatic Adaptation Models 166 9.1 von Kries Model 168 9.2 RetinexTheory 171 9.3 Nayatani et al Model 172 9.4 Guth's Model 174 9.5 Fairchild's Model 177 9.6 Herding CATs 179 9.7 CAT02 181 10 Color Appearance Models 183 10.1 Definition of Color Appearance Models 183 10.2 Construction of Color Appearance Models 184 10.3 CIELAB 185 10.4 Why Not Use Just CIELAB? 193 10.5 What About CIELUV? 194 11 The Nayatani et al. Model 196 11.1 Objectives and Approach 196 11.2 InputData 197 11.3 Adaptation Model 198 11.4 Opponent Color Dimensions 200 11.5 Brightness 201 11.6 Lightness 202 11.7 Hue 202 11.8 Saturation 203 11.9 Chroma 203 11.10 Colorfulness 204 11.11 Inverse Model 204 11.12 Phenomena Predicted 205 11.13 Why Not Use Just the Nayatani et al. Model? 205 12 The Hunt Model 208 12.1 Obj ectives and Approach 208 12.2 Input Data 209 12.3 Adaptation Model 211 12.4 Opponent Color Dimensions 215 12.5 Hue 216 12.6 Saturation 217 12.7 Brightness 218 12.8 Lightness 220 12.9 Chroma 220 12.10 Colorfulness 220 CONTENTS 12.11 Inverse Model 221 12.12 Phenomena Predicted 222 12.13 Why Not Use Just the Hunt Model? 224 13 The RLAB Model 225 13.1 Objectives and Approach 225 13.2 Input Data 227 13.3 Adaptation Model 228 13.4 Opponent Color Dimensions 230 13.5 Lightness 232 13.6 Hue 232 13.7 Chroma 234 13.8 Saturation 234 13.9 Inverse Model 234 13.10 Phenomena Predicted 236 13.11 Why Not Use Just the RLAB Model? 236 14 Other Models 238 14.1 Overview 238 14.2 ATD Model 239 14.3 LLAB Model 245 15 The CIE Color Appearance Model (1997), CIECAM97s 252 15.1 Historical Development, Objectives, and Approach 252 15.2 Input Data 255 15.3 Adaptation Model 255 15.4 Appearance Correlates 257 15.5 Inverse Model 259 15.6 Phenomena Predicted 259 15.7 The ZLAB Color Appearance Model 260 15.8 Why Not Use Just CIECAM97s? 264 16 CIECAM02 265 16.1 Objectives and Approach 265 16.2 Input Data 266 16.3 Adaptation Model 267 16.4 Opponent Color Dimensions 271 16.5 Hue 271 16.6 Lightness 272 16.7 Brightness 272 16.8 Chroma 273 16.9 Colorfulness 273 16.10 Saturation 273 16.11 Cartesian Coordinates 273 16.12 Inverse Model 274 16.13 Implementation Guidelines 274 CONTENTS 16.14 Phenomena Predicted 275 16.15 Why Not Use Just CIECAM02? 275 16.16 Outlook 277 17 Testing Color Appearance Models 278 17.1 Overview 278 17.2 Qualitative Tests 279 17.3 Corresponding Colors Data 283 17.4 Magnitude Estimation Experiments 285 17.5 Direct Model Tests 287 17.6 CIEActMties 291 17.7 A Pictorial Review of Color Appearance Models 295 18 Traditional Colorimetric Applications 299 18.1 Color Rendering 299 18.2 Color Differences 301 18.3 Indices of Metamerism 304 18.4 A General System of Colorimetry? 306 19 Device-independent Color Imaging 308 19.1 The Problem 309 19.2 Levels of Color Reproduction 310 19.3 A Revised Set of Objectives 312 19.4 General Solution 315 19.5 Device Calibration and Characterization 316 19.6 The Need for Color Appearance Models 321 19.7 Definition ofViewing Conditions 321 19.8 Viewing-conditions-independent Color Space 323 19.9 Gamut Mapping 324 19.10 Color Preferences 327 19.11 Inverse Process 328 19.12 Example System 328 19.13 ICC Implementation 330 20 Image Appearance Modeling and The Future 334 20.1 From Color Appearance to Image Appearance 335 20.2 The iCAM Framework 340 20.3 A Modular Image-difference Model 346 2Q.4 Image Appearance and Rendering Applications 350 20.5 Image Difference and Quality Applications 355 20.6 Future Directions 357 References 361 Index 378 .
Load more

Recommended publications
  • Color Models
    Color Models Jian Huang CS456 Main Color Spaces • CIE XYZ, xyY • RGB, CMYK • HSV (Munsell, HSL, IHS) • Lab, UVW, YUV, YCrCb, Luv, Differences in Color Spaces • What is the use? For display, editing, computation, compression, …? • Several key (very often conflicting) features may be sought after: – Additive (RGB) or subtractive (CMYK) – Separation of luminance and chromaticity – Equal distance between colors are equally perceivable CIE Standard • CIE: International Commission on Illumination (Comission Internationale de l’Eclairage). • Human perception based standard (1931), established with color matching experiment • Standard observer: a composite of a group of 15 to 20 people CIE Experiment CIE Experiment Result • Three pure light source: R = 700 nm, G = 546 nm, B = 436 nm. CIE Color Space • 3 hypothetical light sources, X, Y, and Z, which yield positive matching curves • Y: roughly corresponds to luminous efficiency characteristic of human eye CIE Color Space CIE xyY Space • Irregular 3D volume shape is difficult to understand • Chromaticity diagram (the same color of the varying intensity, Y, should all end up at the same point) Color Gamut • The range of color representation of a display device RGB (monitors) • The de facto standard The RGB Cube • RGB color space is perceptually non-linear • RGB space is a subset of the colors human can perceive • Con: what is ‘bloody red’ in RGB? CMY(K): printing • Cyan, Magenta, Yellow (Black) – CMY(K) • A subtractive color model dye color absorbs reflects cyan red blue and green magenta green blue and red yellow blue red and green black all none RGB and CMY • Converting between RGB and CMY RGB and CMY HSV • This color model is based on polar coordinates, not Cartesian coordinates.
    [Show full text]
  • When Red Lights Look Yellow
    Publications 11-2005 When Red Lights Look Yellow Joanne M. Wood Queensland University of Technology David A. Atchison Queensland University of Technology Alex Chaparro Wichita State University, [email protected] Follow this and additional works at: https://commons.erau.edu/publication Part of the Cognition and Perception Commons, Musculoskeletal, Neural, and Ocular Physiology Commons, and the Ophthalmology Commons Scholarly Commons Citation Wood, J. M., Atchison, D. A., & Chaparro, A. (2005). When Red Lights Look Yellow. Investigative Ophthalmology & Visual Science, 46(11). https://doi.org/10.1167/iovs.04-1513 This Article is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Publications by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. When Red Lights Look Yellow Joanne M. Wood,1 David A. Atchison,1 and Alex Chaparro2 PURPOSE. Red signals are typically used to signify danger. This observer’s distance spectacle prescription. It did not occur study was conducted to investigate a situation identified by with lens powers in excess of ϩ1.00 D, as the signals became train drivers in which red signals appear yellow when viewed too blurred for the viewer to distinguish the color. A compre- at long distances (ϳ900 m) through progressive-addition hensive eye and vision examination of the train driver who had lenses. originally reported the color misperception revealed that his METHODS. A laboratory study was conducted to investigate the corrected vision was normal. The train driver was also shown effects of defocus, target size, ambient illumination, and sur- to have normal color vision, as assessed by the Ishihara, Farns- round characteristics on the extent of the color misperception worth Lantern, and Farnsworth D15 tests.
    [Show full text]
  • COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING
    COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING Color space A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components (e.g. RGB and CMYK are color models). However, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with little connection to the requirements of any given application. Adding a certain mapping function between the color model and a certain reference color space results in a definite "footprint" within the reference color space. This "footprint" is known as a gamut, and, in combination with the color model, defines a new color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB model. In the most generic sense of the definition above, color spaces can be defined without the use of a color model. These spaces, such as Pantone, are in effect a given set of names or numbers which are defined by the existence of a corresponding set of physical color swatches. This article focuses on the mathematical model concept. Understanding the concept Most people have heard that a wide range of colors can be created by the primary colors red, blue, and yellow, if working with paints. Those colors then define a color space. We can specify the amount of red color as the X axis, the amount of blue as the Y axis, and the amount of yellow as the Z axis, giving us a three-dimensional space, wherein every possible color has a unique position.
    [Show full text]
  • Several Color Appearance Phenomena in Color Reproduction
    2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT-2012) Several Color Appearance Phenomena in Color Reproduction Qin-ling Dai1,a, Xiao-zhou Li2*,b, Ai Xu2 1 School of Materials Engineering (Southwest Forestry University), Kunming, China 650224 2 Key Laboratory of Pulp & Paper Science and Technology (Shandong Polytechnic University), Ministry of Education, Ji’nan, China, 250353 [email protected], bcorresponding author: [email protected] Keywords: color reproduction, color appearance, color appearance phenomena Abstract. Color perceived performance was influenced by various color appearance phenomena caused by varying viewing conditions in color reproduction process. It is necessary to do some research on the color appearance phenomena to represent the color appearance models qualitatively and quantitatively and accurate color reproduction easily. Only the phenomena were studied thoroughly, could the color transmission and reproduction be well performed. The color appearance and common color appearance phenomena of color reproduction were analyzed in this paper. And the basic theory of color appearance in color reproduction was also studied. Introduction High fidelity digital printing plays an important role in high fidelity color transmission and reproduction and it is one of the most important techniques to perform high fidelity color reproduction. High fidelity digital printing helps to perform accurate color reproduction of the original which can’t be performed because of paper and ink in traditional printing process [1]. In color printing, the color difference caused by paper, ink and viewing conditions is various. The difference is not only colorimetric difference but also different color appearance phenomena. While the different color appearance phenomenon is the leading factor to influence the color vision perceived.
    [Show full text]
  • Prediction of Munsell Appearance Scales Using Various Color-Appearance Models
    Prediction of Munsell Appearance Scales Using Various Color- Appearance Models David R. Wyble,* Mark D. Fairchild Munsell Color Science Laboratory, Rochester Institute of Technology, 54 Lomb Memorial Dr., Rochester, New York 14623-5605 Received 1 April 1999; accepted 10 July 1999 Abstract: The chromaticities of the Munsell Renotation predict the color of the objects accurately in these examples, Dataset were applied to eight color-appearance models. a color-appearance model is required. Modern color-appear- Models used were: CIELAB, Hunt, Nayatani, RLAB, LLAB, ance models should, therefore, be able to account for CIECAM97s, ZLAB, and IPT. Models were used to predict changes in illumination, surround, observer state of adapta- three appearance correlates of lightness, chroma, and hue. tion, and, in some cases, media changes. This definition is Model output of these appearance correlates were evalu- slightly relaxed for the purposes of this article, so simpler ated for their uniformity, in light of the constant perceptual models such as CIELAB can be included in the analysis. nature of the Munsell Renotation data. Some background is This study compares several modern color-appearance provided on the experimental derivation of the Renotation models with respect to their ability to predict uniformly the Data, including the specific tasks performed by observers to dimensions (appearance scales) of the Munsell Renotation evaluate a sample hue leaf for chroma uniformity. No par- Data,1 hereafter referred to as the Munsell data. Input to all ticular model excelled at all metrics. In general, as might be models is the chromaticities of the Munsell data, and is expected, models derived from the Munsell System per- more fully described below.
    [Show full text]
  • Chapter 6 COLOR and COLOR VISION
    Chapter 6 – page 1 You need to learn the concepts and formulae highlighted in red. The rest of the text is for your intellectual enjoyment, but is not a requirement for homework or exams. Chapter 6 COLOR AND COLOR VISION COLOR White light is a mixture of lights of different wavelengths. If you break white light from the sun into its components, by using a prism or a diffraction grating, you see a sequence of colors that continuously vary from red to violet. The prism separates the different colors, because the index of refraction n is slightly different for each wavelength, that is, for each color. This phenomenon is called dispersion. When white light illuminates a prism, the colors of the spectrum are separated and refracted at the first as well as the second prism surface encountered. They are deflected towards the normal on the first refraction and away from the normal on the second. If the prism is made of crown glass, the index of refraction for violet rays n400nm= 1.59, while for red rays n700nm=1.58. From Snell’s law, the greater n, the more the rays are deflected, therefore violet rays are deflected more than red rays. The infinity of colors you see in the real spectrum (top panel above) are called spectral colors. The second panel is a simplified version of the spectrum, with abrupt and completely artificial separations between colors. As a figure of speech, however, we do identify quite a broad range of wavelengths as red, another as orange and so on.
    [Show full text]
  • A Guide to Colorimetry
    A Guide to Colorimetry Sherwood Scientific Ltd 1 The Paddocks Cherry Hinton Road Cambridge CB1 8DH England www.sherwood-scientific.com Tel: +44 (0)1223 243444 Fax: +44 (0)1223 243300 Registered in England and Wales Registration Number 2329039 Reg. Office as above Group I II III IV V VI VII VIII Period 1A 8A 1 2 1 H 2A 3A 4A 5A 6A 7A He 1.008 4.003 3 4 5 6 7 8 9 10 2 Li Be B C N O F Ne 6.939 9.0122 10.811 12.011 14.007 15.999 18.998 20.183 11 12 13 14 15 16 17 18 3 Na Mg 3B 4B 5B 6B 7B [---------------8B-------------] 1B 2B Al Si P S Cl Ar 22.99 24.312 26.982 28.086 30.974 32.064 35.453 39.948 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 4 K Ca Sc Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge As Se Br Kr 39.102 40.08 44.956 47.9 50.942 51.996 54.938 55.847 58.933 58.71 63.546 65.37 69.72 72.59 74.922 78.96 79.904 83.8 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 5 Rb Sr Y Zr Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb Te I Xe 85.47 87.52 88.905 91.22 92.906 95.94 [97] 101.07 102.91 106.4 107.87 112.4 114.82 118.69 121.75 127.6 126.9 131.3 55 56 57* 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 6 Cs Ba La Hf Ta W Re Os Ir Pt Au Hg Ti Pb Bi Po At Rn 132.91 137.34 138.91 178.49 180.95 183.85 186.2 190.2 192.2 195.09 196.97 200.59 204.37 207.19 208.98 209 210 222 87 88 89** 104 105 106 107 108 109 110 111 112 114 116 7 Fr Ra Ac Rt Db Sg Bh Hs Mt 215 226.03 227.03 [261] [262] [266] [264] [269] [268] [271] [272] [277] [289] [289] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 * Lanthanides Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu 140.12 140.91 144.24 145 150.35 151.96 157.25 158.92 152.5 164.93 167.26 168.93 173.04 174.97 90 91 92 93 94 95 96 97 98 99 100 101 102 103 ** Actinides Th Pa U Np Pu Am Cm Bk Cf Es Fm Md No Lr 232.04 231 238 237.05 239.05 241.06 244.06 249.08 251 252.08 257.1 258.1 259.1 262.11 The ability to analyse and quantify colour in aqueous solutions and liquids using a colorimeter is something today’s analyst takes for granted.
    [Show full text]
  • Tabla De Conversión Pantone a NCS (Natural Color System)
    Tabla de conversión Pantone a NCS (Natural Color System) PANTONE NCS (más parecido) PANTONE NCS (más parecido) Pantone Yellow C NCS 0580-Y Pantone 3985C NCS 3060-G80Y Pantone Yellow U NCS 0580-Y Pantone 3985U NCS 4040-G80Y Pantone Warm Red C NCS 0580-Y70R Pantone 3995C NCS 5040-G80Y Pantone Warm Red U NCS 0580-Y70R Pantone 3995U NCS 6020-G70Y Pantone Rubine Red C NCS 1575-R10B Pantone 400C NCS 2005-Y50R Pantone Rubine Red U NCS 1070-R20B Pantone 400U NCS 2502-R Pantone Rhodamine Red C Pantone 401C NCS 2005-Y50R Pantone Rhodamine Red U NCS 1070-R20B Pantone 401U NCS 2502-R Pantone Purple C Pantone 402C NCS 4005-Y50R Pantone Purple U NCS 2060-R40B Pantone 402U NCS 3502-R Pantone Violet C Pantone 403C NCS 4055-Y50R Pantone Violet U NCS 3050-R60B Pantone 403U NCS 4502-R Pantone Reflex Blue C NCS 3560-R80B Pantone 404C NCS 6005-Y20R Pantone Reflex Blue U NCS 3060-R70B Pantone 404U NCS 5502-R Pantone Process Blue C NCS 2065-B Pantone 405C NCS 7005-Y20R Pantone Process Blue U NCS 1565-B Pantone 405U NCS 6502-R Pantone Green C NCS 2060-B90G Pantone 406C NCS 2005-Y50R Pantone Green U NCS 2060-B90G Pantone 406U NCS 2005-Y50R Pantone Black C NCS 8005-Y20R Pantone 407C NCS 3005-Y50R Pantone Black U NCS 7502-Y Pantone 407U NCS 3005-Y80R Pantone Yellow 012C NCS 0580-Y Pantone 408C NCS 3005-Y50R Pantone Yellow 012U NCS 0580-Y Pantone 408U NCS 4005-Y80R Pantone Orange 021C NCS 0585-Y60R Pantone 409C NCS 5005-Y50R Pantone Orange 021U NCS 0580-Y60R Pantone 409U NCS 5005-Y80R Pantone Red 032C NCS 0580-Y90R Pantone 410C NCS 5005-Y50R Pantone Red 032U NCS
    [Show full text]
  • Colornet--Estimating Colorfulness in Natural Images
    COLORNET - ESTIMATING COLORFULNESS IN NATURAL IMAGES Emin Zerman∗, Aakanksha Rana∗, Aljosa Smolic V-SENSE, School of Computer Science, Trinity College Dublin, Dublin, Ireland ABSTRACT learning-based objective metric ‘ColorNet’ for the estimation of colorfulness in natural images. Based on a convolutional neural Measuring the colorfulness of a natural or virtual scene is critical network (CNN), our proposed ColorNet is a two-stage color rating for many applications in image processing field ranging from captur- model, where at stage I, a feature network extracts the characteristics ing to display. In this paper, we propose the first deep learning-based features from the natural images and at stage II, a rating network colorfulness estimation metric. For this purpose, we develop a color estimates the colorfulness rating. To design our feature network, rating model which simultaneously learns to extracts the pertinent we explore the designs of the popular high-level CNN based fea- characteristic color features and the mapping from feature space to ture models such as VGG [22], ResNet [23], and MobileNet [24] the ideal colorfulness scores for a variety of natural colored images. architectures which we finally alter and tune for our colorfulness Additionally, we propose to overcome the lack of adequate annotated metric problem at hand. We also propose a rating network which dataset problem by combining/aligning two publicly available color- is simultaneously learned to estimate the relationship between the fulness databases using the results of a new subjective test which characteristic features and ideal colorfulness scores. employs a common subset of both databases. Using the obtained In this paper, we additionally overcome the challenge of the subjectively annotated dataset with 180 colored images, we finally absence of a well-annotated dataset for training and validating Col- demonstrate the efficacy of our proposed model over the traditional orNet model in a supervised manner.
    [Show full text]
  • Colour Vision Deficiency
    Eye (2010) 24, 747–755 & 2010 Macmillan Publishers Limited All rights reserved 0950-222X/10 $32.00 www.nature.com/eye Colour vision MP Simunovic REVIEW deficiency Abstract effective "treatment" of colour vision deficiency: whilst it has been suggested that tinted lenses Colour vision deficiency is one of the could offer a means of enabling those with commonest disorders of vision and can be colour vision deficiency to make spectral divided into congenital and acquired forms. discriminations that would normally elude Congenital colour vision deficiency affects as them, clinical trials of such lenses have been many as 8% of males and 0.5% of femalesFthe largely disappointing. Recent developments in difference in prevalence reflects the fact that molecular genetics have enabled us to not only the commonest forms of congenital colour understand more completely the genetic basis of vision deficiency are inherited in an X-linked colour vision deficiency, they have opened the recessive manner. Until relatively recently, our possibility of gene therapy. The application of understanding of the pathophysiological basis gene therapy to animal models of colour vision of colour vision deficiency largely rested on deficiency has shown dramatic results; behavioural data; however, modern molecular furthermore, it has provided interesting insights genetic techniques have helped to elucidate its into the plasticity of the visual system with mechanisms. respect to extracting information about the The current management of congenital spectral composition of the visual scene. colour vision deficiency lies chiefly in appropriate counselling (including career counselling). Although visual aids may Materials and methods be of benefit to those with colour vision deficiency when performing certain tasks, the This article was prepared by performing a evidence suggests that they do not enable primary search of Pubmed for articles on wearers to obtain normal colour ‘colo(u)r vision deficiency’ and ‘colo(u)r discrimination.
    [Show full text]
  • 1 Human Color Vision
    CAMC01 9/30/04 3:13 PM Page 1 1 Human Color Vision Color appearance models aim to extend basic colorimetry to the level of speci- fying the perceived color of stimuli in a wide variety of viewing conditions. To fully appreciate the formulation, implementation, and application of color appearance models, several fundamental topics in color science must first be understood. These are the topics of the first few chapters of this book. Since color appearance represents several of the dimensions of our visual experience, any system designed to predict correlates to these experiences must be based, to some degree, on the form and function of the human visual system. All of the color appearance models described in this book are derived with human visual function in mind. It becomes much simpler to understand the formulations of the various models if the basic anatomy, physiology, and performance of the visual system is understood. Thus, this book begins with a treatment of the human visual system. As necessitated by the limited scope available in a single chapter, this treatment of the visual system is an overview of the topics most important for an appreciation of color appearance modeling. The field of vision science is immense and fascinating. Readers are encouraged to explore the liter- ature and the many useful texts on human vision in order to gain further insight and details. Of particular note are the review paper on the mechan- isms of color vision by Lennie and D’Zmura (1988), the text on human color vision by Kaiser and Boynton (1996), the more general text on the founda- tions of vision by Wandell (1995), the comprehensive treatment by Palmer (1999), and edited collections on color vision by Backhaus et al.
    [Show full text]
  • RAINSTONE Inspired by Nature, This Collection Will Give Fresh and Natural Design Elements to Any Space
    RAINSTONE Inspired by nature, this collection will give fresh and natural design elements to any space. Rain Stone Dark Grey 60x60 cm | 24”x 24” Dark Grey Rain Stone 12 13 Floor Floor Floor Rain Stone_Natural 60x60 | 24”x24” 14 15 RAIN STONE NATURAL RAIN STONE BEIGE WHITE *CERST060002_NATURAL 60x120 - 24”x48” rect. CERST060006_NATURAL 60x60 - 24”x24” rect. *CERST060001_BEIGE WHITE 60x120 - 24”x48” rect. CERST060005_BEIGE WHITE 60x60 - 24”x24” rect. *CERST030014 _NATURAL 30x120 - 12”x48” rect. CERST030002_NATURAL 30x60 - 12”x24” rect. *CERST030013 _BEIGE WHITE 30x120 - 12”x48” rect. CERST030001_BEIGE WHITE 30x60 - 12”x24” rect. *CERST029002_NATURAL 29x29 - 12”x12” rect. CERST005002_NATURAL MOSAICO *CERST029001_BEIGE WHITE 29x29 - 12”x12” rect. CERST005001_BEIGE WHITE MOSAICO 5x5 - 2”x2” rect. 5x5 - 2”x2” rect. *CERST015002_NATURAL 15x60 - 6”x24” rect. *CERST010002_NATURAL 10x30 - 4”x12” rect. *CERST015001_BEIGE WHITE 15x60 - 6”x24” rect. *CERST010001_BEIGE WHITE 10x30 - 4”x12” rect. *CERST030006_NATURAL *CERST030010_NATURAL WALL *CERST030005_BEIGE WHITE *CERST030009_ BEIGE WHITE WALL 5x15 - 2”x6” rect. 30x60 - 12”x24” rect. 5x15 - 2”x6” rect. 30x60 - 12”x24” rect. 30x30 - 12”x12” su rete rect. 30x30 - 12”x12” su rete rect. *CERST031002 _ NATURAL CHEVRON *CERST028002 _ NATURAL FRINGE *CERST031001_ BEIGE WHITE CHEVRON *CERST028001_ BEIGE WHITE FRINGE 31,5x29,6 - 12,40”x11,65” rect. 28,8X28,8 - 11,34”x11,34” rect. 31,5x29,6 - 12,40”x11,65” rect. 28,8X28,8 - 11,34”x11,34” rect. 16 * Special order sizes 17 RAIN STONE LIGHT GREY RAIN STONE DARK GREY *CERST060003_LIGHT GREY 60x120 - 24”x48” rect. CERST060007_LIGHT GREY 60x60 - 24”x24” rect. *CERST060004_DARK GREY 60x120 - 24”x48” rect. CERST060008_DARK GREY 60x60 - 24”x24” rect.
    [Show full text]