Color Theory History

Total Page:16

File Type:pdf, Size:1020Kb

Color Theory History Color Theory History By Adrian and Kris4na Color Spectrum and the 1700’s • Isaac Newton • Our modern theory of light and color started with Newton. • He was the first person to really understand how a rainbow was made and the first to make a color wheel. • The understanding of color theory started with Newton in the 1660’s doing different experiments. An important experiment that Newton performed was seng up a prism by a window as the sunlight hit the prism the prism projected a color spectrum. Color Spectrum and the 1700’s •Color Wheel Model • The prism experiment helped Newton built a concept of what is known as the color wheel. • He arranged colors around the circumstance of a circle (boPom leQ), this helped ar4sts because it arranged the primary colors opposite to their complementary colors. • Claude Boute’s color model (right) was the first to represent Newton’s idea. Tobias Mayer and the 1700’s • In 1758 Tobias Mayer did a color triangle diagram and he began with 3 main pure colors, red, blue and yellow. • He put them on each corner creang a triangle shape. The colors gradate towards the center, showing that 2 colors can create another one. Schaffer and the 1700’s • Jacob Chris4an Schaffer • 1769 Schaffer (right) who was naturalist and inventor invented his own color system. • He explained how when blue, red, and yellow are combined they create mul4ple shades in between. His color system shows color combinaons within a color group. Goethe's Color Theory and the 1800’s • Johann Wolfgang Van Geothe (1810) was a writher and scien4st • He studied the psychological impact that colors had an people’s emo4ons and feelings. • Geothe created different theories about each color. For example he suggested that yellow since is the closes color to the light and is a bright color it creates a serene, and joyful feeling • Newton believed color wasn’t a physiological process but a measurement according to white light, making color a physical object. • Geothe on the other hand believed that color wasn’t just a physical object or a measurement of light but a mixture of light and dark. Runge and Chevreul 1800’s • In 1807 Painter OPo Runge made a color wheel model taking the primary colors and adding black and white to demonstrate other colors. • He created a 3D model color sphere (right). • In 1839 Michel Eugene Chevreul took Runge's idea and arranged 72 colors into a hemisphere ,similar to Mayer's arrangement. • Chevreul also came up with a phenomenon called the Chevreul's Illusion, 2 same colors of different intensi4es are placed next to each other, and they seem to be brighter at the edge where they meet. Munsell and the 1900’s • In 1915 Albert Henry Munsell came up with a cylindrical system model showing hue, value, and chroma (saturaon). • His model described the colors in a scien4fic manner. • His color wheel helped launch other color wheels. • In his color model chroma is demonstrated horizontal, value is ver4cal, and hue is shown around the model. THE NATURAL COLOR SYSTEM, 1900’s • In 1979 Ewald Hering came up with the Natural Color System. • The color model was based on the six psychological primary colors of yellow, blue, red, green, white and black. • The color wheel that was created was not designed for mixing colors but for organizing them in relaon to how they are experienced by people. • It proved to be useless to designers and ar4st because it had a more natural approach when it came down to color mixture. • Today, It is the most recognized color-matching system around the world. .
Loading...
Loading...
Loading...
Loading...
Loading...

4 pages remaining, click to 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]
  • Chapter 6 : Color Image Processing
    Chapter 6 : Color Image Processing CCU, Taiwan Wen-Nung Lie Color Fundamentals Spectrum that covers visible colors : 400 ~ 700 nm Three basic quantities Radiance : energy that flows from the light source (measured in Watts) Luminance : a measure of energy an observer perceives from a light source (in lumens) Brightness : a subjective descriptor difficult to measure CCU, Taiwan Wen-Nung Lie 6-1 About human eyes Primary colors for standardization blue : 435.8 nm, green : 546.1 nm, red : 700 nm Not all visible colors can be produced by mixing these three primaries in various intensity proportions Cones in human eyes are divided into three sensing categories 65% are sensitive to red light, 33% sensitive to green light, 2% sensitive to blue (but most sensitive) The R, G, and B colors perceived by CCU, Taiwan human eyes cover a range of spectrum Wen-Nung Lie 6-2 Primary and secondary colors of light and pigments Secondary colors of light magenta (R+B), cyan (G+B), yellow (R+G) R+G+B=white Primary colors of pigments magenta, cyan, and yellow M+C+Y=black CCU, Taiwan Wen-Nung Lie 6-3 Chromaticity Hue + saturation = chromaticity hue : an attribute associated with the dominant wavelength or dominant colors perceived by an observer saturation : relative purity or the amount of white light mixed with a hue (the degree of saturation is inversely proportional to the amount of added white light) Color = brightness + chromaticity Tristimulus values (the amount of R, G, and B needed to form any particular color : X, Y, Z trichromatic
    [Show full text]
  • Chapter 2 Fundamentals of Digital Imaging
    Chapter 2 Fundamentals of Digital Imaging Part 4 Color Representation © 2016 Pearson Education, Inc., Hoboken, 1 NJ. All rights reserved. In this lecture, you will find answers to these questions • What is RGB color model and how does it represent colors? • What is CMY color model and how does it represent colors? • What is HSB color model and how does it represent colors? • What is color gamut? What does out-of-gamut mean? • Why can't the colors on a printout match exactly what you see on screen? © 2016 Pearson Education, Inc., Hoboken, 2 NJ. All rights reserved. Color Models • Used to describe colors numerically, usually in terms of varying amounts of primary colors. • Common color models: – RGB – CMYK – HSB – CIE and their variants. © 2016 Pearson Education, Inc., Hoboken, 3 NJ. All rights reserved. RGB Color Model • Primary colors: – red – green – blue • Additive Color System © 2016 Pearson Education, Inc., Hoboken, 4 NJ. All rights reserved. Additive Color System © 2016 Pearson Education, Inc., Hoboken, 5 NJ. All rights reserved. Additive Color System of RGB • Full intensities of red + green + blue = white • Full intensities of red + green = yellow • Full intensities of green + blue = cyan • Full intensities of red + blue = magenta • Zero intensities of red , green , and blue = black • Same intensities of red , green , and blue = some kind of gray © 2016 Pearson Education, Inc., Hoboken, 6 NJ. All rights reserved. Color Display From a standard CRT monitor screen © 2016 Pearson Education, Inc., Hoboken, 7 NJ. All rights reserved. Color Display From a SONY Trinitron monitor screen © 2016 Pearson Education, Inc., Hoboken, 8 NJ.
    [Show full text]
  • Light and Color
    Chapter 9 LIGHT AND COLOR What Is Color? Color is a human phenomenon. To the physicist, the only difference be- tween light with a wavelength of 400 nanometers and that of 700 nm is Different wavelengths wavelength and amount of energy. However a normal human eye will see cause the eye to see another very significant difference: The shorter wavelength light will different colors. cause the eye to see blue-violet and the longer, deep red. Thus color is the response of the normal eye to certain wavelengths of light. It is nec- essary to include the qualifier “normal” because some eyes have abnor- malities which makes it impossible for them to distinguish between certain colors, red and green, for example. Note that “color” is something that happens in the human seeing ap- Only light itself paratus—when the eye perceives certain wavelengths of light. There is causes sensations of no mention of paint, pigment, ink, colored cloth or anything except light color. itself. Clear understanding of this point is vital to the forthcoming discus- sion. Colorants by themselves cannot produce sensations of color. If the proper light waves are not present, colorants are helpless to produce a sensation of color. Thus color resides in the eye, actually in the retina- optic-nerve-brain combination which teams up to provide our color sen- Color vision is sations. How this system works has been a matter of study for many years complex and not and recent investigations, many of them based on the availability of new completely brain scanning machines, have made important discoveries.
    [Show full text]
  • Computational RYB Color Model and Its Applications
    IIEEJ Transactions on Image Electronics and Visual Computing Vol.5 No.2 (2017) -- Special Issue on Application-Based Image Processing Technologies -- Computational RYB Color Model and its Applications Junichi SUGITA† (Member), Tokiichiro TAKAHASHI†† (Member) †Tokyo Healthcare University, ††Tokyo Denki University/UEI Research <Summary> The red-yellow-blue (RYB) color model is a subtractive model based on pigment color mixing and is widely used in art education. In the RYB color model, red, yellow, and blue are defined as the primary colors. In this study, we apply this model to computers by formulating a conversion between the red-green-blue (RGB) and RYB color spaces. In addition, we present a class of compositing methods in the RYB color space. Moreover, we prescribe the appropriate uses of these compo- siting methods in different situations. By using RYB color compositing, paint-like compositing can be easily achieved. We also verified the effectiveness of our proposed method by using several experiments and demonstrated its application on the basis of RYB color compositing. Keywords: RYB, RGB, CMY(K), color model, color space, color compositing man perception system and computer displays, most com- 1. Introduction puter applications use the red-green-blue (RGB) color mod- Most people have had the experience of creating an arbi- el3); however, this model is not comprehensible for many trary color by mixing different color pigments on a palette or people who not trained in the RGB color model because of a canvas. The red-yellow-blue (RYB) color model proposed its use of additive color mixing. As shown in Fig.
    [Show full text]
  • Goethe's Theory of Colors Between the Ancient Philosophy, Middle Ages
    CULTURE, MEDIA & FILM | RESEARCH ARTICLE Goethe’s theory of colors between the ancient philosophy, middle ages occultism and modern science Victor Barsan and Andrei Merticariu Cogent Arts & Humanities (2016), 3: 1145569 Page 1 of 29 Barsan & Merticariu, Cogent Arts & Humanities (2016), 3: 1145569 http://dx.doi.org/10.1080/23311983.2016.1145569 CULTURE, MEDIA & FILM | RESEARCH ARTICLE Goethe’s theory of colors between the ancient philosophy, middle ages occultism and modern science 1 2 Received: 18 February 2015 Victor Barsan * and Andrei Merticariu Accepted: 20 January 2016 Published: 18 February 2016 Abstract: Goethe’s rejection of Newton’s theory of colors is an interesting example *Corresponding author: Victor Barsan, of the vulnerability of the human mind—however brilliant it might be—to fanati- Department of Theoretical Physics, cism. After an analysis of Goethe’s persistent fascination with magic and occultism, Horia Hulubei Institute of Physics and Nuclear Engineering, Aleea Reactorului of his education, existential experiences, influences, and idiosyncrasies, the authors nr. 30, Magurele, Bucharest, Romania E-mail: vbarsan@theory.nipne.ro propose an original interpretation of his anti-Newtonian position. The relevance of Goethe’s Farbenlehre to physics and physiology, from the perspective of modern sci- Reviewing editor: Peter Stanley Fosl, Transylvania ence, is discussed in detail. University, USA Subjects: Aristotle; Biophysics; Experimental Physics; Fine Art; Medical Physics; Ophthal- Additional information is available at the end of the article mology; Philosophy of Art; Philosophy of Science; Presocratics Keywords: ancient philosophy; Greek–Roman classicism; middle ages science; Newtonian science; occultism; pantheism; optics; theory of colors; primordial phenomenon (urphaeno men) 1. Introduction Light is one of the most interesting components of the physical universe.
    [Show full text]
  • Fuzzy Set Theoretical Approach to the Tone Triangular System
    JOURNAL OF COMPUTERS, VOL. 6, NO. 11, NOVEMBER 2011 2345 Fuzzy Set Theoretical Approach to the Tone Triangular System Naotoshi Sugano Tamagawa University, Tokyo, Japan sugano@eng.tamagawa.ac.jp Abstract—The present study considers a fuzzy color system gravity of the attribute information of vague colors. This in which three input fuzzy sets are constructed on the tone fuzzy set theoretical approach is useful for vague color triangle. This system can process a fuzzy input to a tone information processing, color identification, and similar triangular system and output to a color on the RGB applications. triangular system. Three input fuzzy sets (not black, white, and light) are applied to the tone triangle relationship. By treating three attributes of chromaticness, whiteness, and II. METHODS blackness on the tone triangle, a target color can be easily A. Color Triangle and Additive Color Mixture obtained as the center of gravity of the output fuzzy set. In Additive color mixing occurs when two or three beams the present paper, the differences between fuzzy inputs and inference outputs are described, and the relationship of differently colored light combine. It has been found between inference outputs for crisp inputs and for fuzzy that mixing just three additive primary colors, red, green, inputs on the RGB triangular system are shown by the and blue, can produce the majority of colors. In general, a input-output characteristics between chromaticness, color vector can be described by certain quantities as a whiteness, and blackness as the inputs and redness (as one scalar and a direction. These quantities are referred to as of the outputs).
    [Show full text]
  • Image Processing Based Automatic Color Inspection and Detection of Colored Wires in Electric Cables
    International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 5 (2017) pp. 611-617 © Research India Publications. http://www.ripublication.com Image Processing based Automatic Color Inspection and Detection of Colored Wires in Electric Cables 1Rajalakshmi M, 2Ganapathy V, 3Rengaraj R and 4Rohit D 1Assistant Professor, 2Professor, Dept. of IT., SRM University, Kattankulathur-603203, Tamil Nadu, India. 3Associate Professor, Dept. of EEE, SSN College of Engg., Kalavakkam-603110, Tamil Nadu, India. 4Research Associate, Siechem Wires and Cables, Pondicherry, India. Abstract manipulation and interpretation of visual information, and it plays an increasingly important role in our daily life. Also it In this paper, an automatic visual inspection system using is applied in a variety of disciplines and fields in science and image processing techniques to check the consistency of technology. Some of the applications are television, color of the wire after insulation, and meeting the photography, robotics, remote sensing, medical diagnosis requirements of the manufacturer, is presented. Also any and industrial inspection. Probably the most powerful image color irregularities occurring across the insulation are processing system is the human brain together with the eye. displayed. The main contributions of this paper are: (i) the The system receives, enhances and stores images at self-learning system, which does not require manual enormous rates of speed. The objective of image processing intervention and (ii) a color detection algorithm that can be is to visually enhance or statistically evaluate some aspect of able to meet up with varied finishing of the wire insulation. an image not readily apparent in its original form.
    [Show full text]
  • A Correlated Color Temperature for Illuminants
    . (R P 365) A CORRELATED COLOR TEMPERATURE FOR ILLUMINANTS By Raymond Davis ABSTRACT As has long been known, most of the artificial and natural illuminants do not match exactly any one of the Planckian colors. Therefore, strictly speaking, they can not be assigned a color temperature. A color of this type may, however, be correlated with a representative Planckian color. The method of determining correlated color temperature described in this paper consists in comparing the relative luminosities of each of the three primary red, green, and blue components of the source with similar values for the Planckian series. With such a comparison three component temperatures are obtained; that is, the red component of the source corresponds with that of the Planckian radiator at one temperature, its green component with that of the Planckian radiator at a second temperature, and its blue component with that of the Planckian radiator at a third temperature. The average of these three component temperatures is designated as the correlated color temperature of the source. The mean devia- tion of the component temperatures from the average temperature is used as a basis for specifying the color (chromaticity) departure of the source from that of the Planckian radiator at the correlated color temperature. The conjunctive wave length indicates the kind of color departure. CONTENTS Page I. Introduction 659 II. The proposed method 662 III. Procedure 665 1. The Planckian radiator evaluated in terms of relative lumi- nosity of the primary components 665 2. Computation of the correlated color temperature 670 3. Calculation of color departure in terms of sensation steps 672 4.
    [Show full text]
  • Arxiv:1902.00267V1 [Cs.CV] 1 Feb 2019 Fcmue Iin Oto H Aaesue O Mg Classificat Image Th for in Used Applications Datasets Fundamental the Most of the Most Vision
    ColorNet: Investigating the importance of color spaces for image classification⋆ Shreyank N Gowda1 and Chun Yuan2 1 Computer Science Department, Tsinghua University, Beijing 10084, China sny17@mails.tsinghua.edu.cn 2 Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China yuanc@sz.tsinghua.edu.cn Abstract. Image classification is a fundamental application in computer vision. Recently, deeper networks and highly connected networks have shown state of the art performance for image classification tasks. Most datasets these days consist of a finite number of color images. These color images are taken as input in the form of RGB images and clas- sification is done without modifying them. We explore the importance of color spaces and show that color spaces (essentially transformations of original RGB images) can significantly affect classification accuracy. Further, we show that certain classes of images are better represented in particular color spaces and for a dataset with a highly varying number of classes such as CIFAR and Imagenet, using a model that considers multi- ple color spaces within the same model gives excellent levels of accuracy. Also, we show that such a model, where the input is preprocessed into multiple color spaces simultaneously, needs far fewer parameters to ob- tain high accuracy for classification. For example, our model with 1.75M parameters significantly outperforms DenseNet 100-12 that has 12M pa- rameters and gives results comparable to Densenet-BC-190-40 that has 25.6M parameters for classification of four competitive image classifica- tion datasets namely: CIFAR-10, CIFAR-100, SVHN and Imagenet. Our model essentially takes an RGB image as input, simultaneously converts the image into 7 different color spaces and uses these as inputs to individ- ual densenets.
    [Show full text]
  • Raphics & Visualization
    Graphics & Visualization Chapter 11 COLOR IN GRAPHICS & VISUALIZATION Graphics & Visualization: Principles & Algorithms Chapter 11 Introduction • The study of color, and the way humans perceive it, a branch of: Physics Physiology Psychology Computer Graphics Visualization • The result of graphics or visualization algorithms is a color or grayscale image to be viewed on an output device (monitor, printer) Graphics programmer should be aware of the fundamental principles behind color and its digital representation Graphics & Visualization: Principles & Algorithms Chapter 11 2 Grayscale • Intensity: achromatic light; color characteristics removed • Intensity can be represented by a real number between 0 (black) and 1 (white) Values between these two extremes are called grayscales • Assume use of d bits to represent the intensity of each pixel n=2d different intensity values per pixel • Question: which intensity values shall we represent ? • Answer: Linear scale of intensities between the minimum & maximum value, is not a good idea: Human eye perceives intensity ratios rather than absolute intensity values. Light bulb example: 20-40-60W Therefore, we opt for a logarithmic distribution of intensity values Graphics & Visualization: Principles & Algorithms Chapter 11 3 Grayscale (2) • Let Φ0 be the minimum intensity value For typical monitors: Φ0 = (1/300) * maximum value 1 (white) Such monitors have a dynamic range of 300:1 • Let λ be the ratio between successive intensity values • Then we take: Φ1 = λ* Φ0 2 Φ1 = λ* Φ1=λ *Φ0 …
    [Show full text]
  • 14. Color Mapping
    14. Color Mapping Jacobs University Visualization and Computer Graphics Lab Recall: RGB color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 691 CMY color model • The CMY color model is related to the RGB color model. •Itsbasecolorsare –cyan(C) –magenta(M) –yellow(Y) • They are arranged in a 3D Cartesian coordinate system. • The scheme is subtractive. Jacobs University Visualization and Computer Graphics Lab Data Analytics 692 Subtractive color scheme • CMY color model is subtractive, i.e., adding colors makes the resulting color darker. • Application: color printers. • As it only works perfectly in theory, typically a black cartridge is added in practice (CMYK color model). Jacobs University Visualization and Computer Graphics Lab Data Analytics 693 CMY color cube • All colors c that can be generated are represented by the unit cube in the 3D Cartesian coordinate system. magenta blue red black grey white cyan yellow green Jacobs University Visualization and Computer Graphics Lab Data Analytics 694 CMY color cube Jacobs University Visualization and Computer Graphics Lab Data Analytics 695 CMY color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 696 CMYK color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 697 Conversion • RGB -> CMY: • CMY -> RGB: Jacobs University Visualization and Computer Graphics Lab Data Analytics 698 Conversion • CMY -> CMYK: • CMYK -> CMY: Jacobs University Visualization and Computer Graphics Lab Data Analytics 699 HSV color model • While RGB and CMY color models have their application in hardware implementations, the HSV color model is based on properties of human perception. • Its application is for human interfaces. Jacobs University Visualization and Computer Graphics Lab Data Analytics 700 HSV color model The HSV color model also consists of 3 channels: • H: When perceiving a color, we perceive the dominant wavelength.
    [Show full text]