Light, Intensities and Color Models 1. Properties of Light 2. Color Models
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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. -
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 -
Painting Part 3 1
.T 720 (07) | 157 ) v.10 • pt. 3 I n I I I 6 International Correspondence Schools, Scranton, Pa. Painting By DURWARD E. NICHOLSON Technical Writer, International Correspondence Schools and DAVID T. JONES, B.Arch. Director, School of Architecture and the Building Trades International Correspondence Schools 6227C Part 3 Edition 1 International Correspondence Schools, Scranton, Pennsylvania International Correspondence Schools, Canadian, Ltd., Montreal, Canadc Painting \A jo Pa3RT 3 “I find in life lliat most affairs tliat require serious handling are distasteful. For this reason, I have always believed that the successful man has the hardest battle with himself rather than with the other fellow'. By To bring one’s self to a frame of mind and to the proper energy to accomplish things that require plain DURWARD E. NICHOLSON hard work continuously is the one big battle that Technical Writer everyone has. When this battle is won for all time, then everything is easy.” \ International Correspondence Schools —Thomas A. Buckner and DAVID T. JONES, B. Arch. 33 Director, School of Architecture and the Building Trades International Correspondence Schools B Member, American Institute of Architects Member, Construction Specifications Institute Serial 6227C © 1981 by INTERNATIONAL TEXTBOOK COMPANY Printed in the United States of America All rights reserved International Correspondence Schools > Scranton, Pennsylvania\/ International Correspondence Schools Canadian, Ltd. ICS Montreal, Canada ▼ O (7M V\) v-V*- / / *r? 1 \/> IO What This Text Covers . /’ v- 3 Painting Part 3 1. PlGX OLORS ___________________ ________ Pages 1 to 14 The color of paint depends on the colors of the pigments that are mixed with the vehicle. -
Color Basics for Landscapes
Landscapae Nov. 2006 L-18 Color Basics for Landscapes Melvin Wong, Department of Tropical Plant and Soil Sciences he topic of color can become very complicated. This Twelve hues of the color wheel Tpublication introduces some basic concepts of color theory and some ideas about our reactions to color that Primary colors may be applicable to choices we make when choosing Yellow plants for landscapes. Red We see only a small portion of the total electromag Blue netic spectrum. We cannot see ultraviolet or infrared light, X-rays, gamma rays, microwaves, radar, or waves Secondary colors from television or FM, AM, or short-wave radio. What Orange (yellow-red) we perceive as “visible” is light waves of a limited range Purple (red-blue) of wavelengths. We “see” these light waves when they Green (blue-yellow) are reflected from objects to the retinas at the back of our eyes. We see black when nothing is reflected, and Tertiary colors white when all visible-wavelength light is reflected. We Yellow-orange (saffron or amber) see red when all light is absorbed except red, blue when Orange-red (vermillion or terra-cotta) all light is absorbed except blue, and yellow when all Red-purple (magenta or fuchsia) light is absorbed except yellow. Purple-blue (navy or royal blue) Twelve basic colors form the color wheel, which was Blue-green (teal) invented by color theorists to help us think about color. Green-yellow (chartreuse) The twelve colors are called hues. Three colors (yellow, red, and blue) are called primary colors because you cannot mix other colors to make them. -
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. -
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. -
Module 3 Imitative and Decorative Arts
TRADE OF PAINTING & DECORATING PHASE 2 Module 3 Imitative and Decorative Arts UNIT: 1 Colour Module 3 – Unit 1 Colour Table of Contents Introduction .............................................................................................................. 1 Learning Outcomes .................................................................................................. 2 1.0 Pigmental, primary, secondary & tertiary colours ...................................... 3 1.1 The colour circle ......................................................................................... 3 1.2 Primary, secondary and tertiary colours .................................................. 5 2.0 Monochromatic, Analogous & Complementary Colour Harmony ............... 6 2.1 Monochromatic, analogous and complementary colour harmony ...... 6 2.2 Colour in decoration................................................................................... 7 3.0 Produce Secondary and Tertiary Colours By Colour Mixing .......................... 8 3.1 Mixing primary colours to produce secondary ....................................... 8 3.2 Mixing secondary colours to produce tertiary colours .......................... 8 4.0 Tints and Shades ............................................................................................. 9 4.1 Define the terms tints and shades ............................................................ 9 4 2 Mixing tints and shades of various colours ............................................. 9 4.3 Colour terminology .................................................................................. -
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. -
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 [email protected] 2 Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China [email protected] 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. -
12. Optical Data Storage
12. Optical data storage 12.1 Theory of color RGB additive color model B B Blue Magenta (0.6, 0.2, 1) (0,0,1) (1,0,1) Gray shades Cyan (g,g,g) (0,1,1) White (1,1,1) (0.6, 1, 0.7) R R Black Red (0,0,0) (1,0,0) G Green (0,1,0) (1, 0.7, 0.1) Yellow (1,1,0) G The RGB color model converts the additive color mixing system into a digital system, in which any color is represented by a point in a color tridimensional space and thus by a three coordinates vector. The coordinates are composed of the respective proportions of the primary light colors (Red, Green, Blue). The RGB model is often used by software as it requires no conversion in order to display colors on a computer screen. 169 CMY subtractive color model Y Y Yellow Green (0, 0.3, 0.9) (0,0,1) (1,0,1) Gray shades Red (g,g,g) (0,1,1) Black (1,1,1) C (0.4, 0, 0.3) C White Cyan (0,0,0) (1,0,0) M Magenta (0,1,0) (0.4, 0.8, 0) Blue (1,1,0) M The CMY color model corresponds to the subtractive color mixing system. The coordinates of a point in this color space are composed of the respective proportions of the primary filter colors (Cyan, Magenta, Yellow). The CMY model is used whenever a colored document is printed. The conversion RGB and CMY models can be written as follows: For the orange point, for example: 170 HSV color model V Green Yellow 120° 60° (37°, 0.9, 1) White H 1.0 S Cyan Red 180° 0° V Blue Magenta 240° 300° Hue is given in polar coordinate. -
Color Book Unformatted
New & Revised - Includes Genesis Heat Set Oils 1 PREFACE There seems to be a mystique about mixing colors. The novice painter procrastinates or delgates this responsibility to a paint manufacturer bu tultimately the day arrives when the painter wants to take control of the palette colours. This can happen only when the painter takes the responsi- bility to learn to mix colour. Mixing colour is very exciting. The knowledge of how to control and create colour is the ultimate in creativity. It is not difficult to master, but it will take time, patience, practice, but most of all desire. The intent of this book is to provide a guide to mixing colour. It is my hope that it will be useful regardless of whether the preferred medium is oil, alkyd, acrylic, watercolour, pastel, pencil or coloured ink. Colour theory applies to all media. All that differs is technique. Colour is so closely interwoven with the other four basic elements of art that it would be impossible to discuss colour without examining its rela- tionship to line, form, texture and space. The importance of colour to har- mony and composition are also inseparable, as are chemistry and tem- perature. Please understand that colour reproduction of a paint mixture in printed form is almost always inaccurate. Every effort has been made to provide the highest quality of photography, colour separations and printing. How- ever, it is not possible to duplicate the myriad nuances of paint with only five colours of printer’s ink. Furthermore, oil reflects the light differently than acrylic. Pencil and watercolour have their own individual properties. -
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.