Color Models and Color Applications
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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 -
In Concert with Teaching Strategies That Have a Solid Theoretical Basis
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in “Teaching and Learning Technology”. In Section 1 of this course you will cover these topics: Learning And Instruction Computer Applications In Education The Impact Of The Computer On Education Topic : Learning And Instruction Topic Objective: At the end of this topic student would be able to understand: Computer's Role In Instruction Instructional Technology Early Applications The Internet Era Outcomes Research Social Context Gagne's Nine Events of Instruction Definition/Overview: The first topic establishes a framework for looking at the computer's role in instruction and examines its role in student learning. A brief review of behaviorist and constructivist theories of instruction and learning is presented. The intent is to demonstrate that the computer can be a practical tool usedWWW.BSSVE.IN in concert with teaching strategies that have a solid theoretical basis. We recognize that thinking patterns and learning styles vary and that many different cognitive processes and intelligences should be valued. This topic presents a brief overview of types of intelligences, perception and motivation in order to emphasize the importance of analyzing student populations and matching instructional materials to student needs. Recognizing the important role that software plays in instruction and learning, a good deal of discussion takes place on the selection and evaluation of effective software. www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 2 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Key Points: 1.Computer's Role in Instruction American education has long incorporated technology in K-12 classrooms tape recorders, televisions, calculators, computers, and many others. -
PANTONE® Colorwebtm 1.0 COLORWEB USER MANUAL
User Manual PANTONE® ColorWebTM 1.0 COLORWEB USER MANUAL Copyright Pantone, Inc., 1996. All rights reserved. PANTONE® Computer Video simulations used in this product may not match PANTONE®-identified solid color standards. Use current PANTONE Color Reference Manuals for accurate color. All trademarks noted herein are either the property of Pantone, Inc. or their respective companies. PANTONE® ColorWeb™, ColorWeb™, PANTONE Internet Color System™, PANTONE® ColorDrive®, PANTONE Hexachrome™† and Hexachrome™ are trademarks of Pantone, Inc. Macintosh, Power Macintosh, System 7.xx, Macintosh Drag and Drop, Apple ColorSync and Apple Script are registered trademarks of Apple® Computer, Inc. Adobe Photoshop™ and PageMill™ are trademarks of Adobe Systems Incorporated. Claris Home Page is a trademark of Claris Corporation. Netscape Navigator™ Gold is a trademark of Netscape Communications Corporation. HoTMetaL™ is a trademark of SoftQuad Inc. All other products are trademarks or registered trademarks of their respective owners. † Six-color Process System Patent Pending - Pantone, Inc.. PANTONE ColorWeb Team: Mark Astmann, Al DiBernardo, Ithran Einhorn, Andrew Hatkoff, Richard Herbert, Rosemary Morretta, Stuart Naftel, Diane O’Brien, Ben Sanders, Linda Schulte, Ira Simon and Annmarie Williams. 1 COLORWEB™ USER MANUAL WELCOME Thank you for purchasing PANTONE® ColorWeb™. ColorWeb™ contains all of the resources nec- essary to ensure accurate, cross-platform, non-dithered and non-substituting colors when used in the creation of Web pages. ColorWeb works with any Web authoring program and makes it easy to choose colors for use within the design of Web pages. By using colors from the PANTONE Internet Color System™ (PICS) color palette, Web authors can be sure their page designs have rich, crisp, solid colors, no matter which computer platform these pages are created on or viewed. -
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. -
Camera Raw Workflows
RAW WORKFLOWS: FROM CAMERA TO POST Copyright 2007, Jason Rodriguez, Silicon Imaging, Inc. Introduction What is a RAW file format, and what cameras shoot to these formats? How does working with RAW file-format cameras change the way I shoot? What changes are happening inside the camera I need to be aware of, and what happens when I go into post? What are the available post paths? Is there just one, or are there many ways to reach my end goals? What post tools support RAW file format workflows? How do RAW codecs like CineForm RAW enable me to work faster and with more efficiency? What is a RAW file? In simplest terms is the native digital data off the sensor's A/D converter with no further destructive DSP processing applied Derived from a photometrically linear data source, or can be reconstructed to produce data that directly correspond to the light that was captured by the sensor at the time of exposure (i.e., LOG->Lin reverse LUT) Photometrically Linear 1:1 Photons Digital Values Doubling of light means doubling of digitally encoded value What is a RAW file? In film-analogy would be termed a “digital negative” because it is a latent representation of the light that was captured by the sensor (up to the limit of the full-well capacity of the sensor) “RAW” cameras include Thomson Viper, Arri D-20, Dalsa Evolution 4K, Silicon Imaging SI-2K, Red One, Vision Research Phantom, noXHD, Reel-Stream “Quasi-RAW” cameras include the Panavision Genesis In-Camera Processing Most non-RAW cameras on the market record to 8-bit YUV formats -
A Method of Content-Based Image Retrieval for the Generation of Image Mosaics
University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2007 A Method Of Content-based Image Retrieval For The Generation Of Image Mosaics Michael Snead University of Central Florida Part of the Computer Engineering Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation Snead, Michael, "A Method Of Content-based Image Retrieval For The Generation Of Image Mosaics" (2007). Electronic Theses and Dissertations, 2004-2019. 3358. https://stars.library.ucf.edu/etd/3358 A METHOD OF CONTENT-BASED IMAGE RETRIEVAL FOR THE GENERATION OF IMAGE MOSAICS by MICHAEL CHRISTOPHER SNEAD B.S. University of Central Florida, 2005 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the School of Electrical Engineering and Computer Science in the College of Engineering & Computer Science at the University of Central Florida Orlando, Florida Spring Term 2007 © 2007 Michael Christopher Snead ii ABSTRACT An image mosaic is an artistic work that uses a number of smaller images creatively combined together to form another larger image. Each building block image, or tessera, has its own distinctive and meaningful content, but when viewed from a distance the tesserae come together to form an aesthetically pleasing montage. This work presents the design and implementation of MosaiX, a computer software system that generates these image mosaics automatically. -
First Semester
NATIONAL UNIVERSITY F o u r t h Y e a r S y l l a b u s D e p a r t m e n t of Computer Science and Engineering Four Year B.Sc. Honours Course Effective from the Session: 2017–2018 National University Subject: Computer Science and Engineering Syllabus for Four Year B.Sc. Honours Course Effective from the Session: 2017-2018 Year wise courses and marks distribution FOURTH YEAR Semester VII Course Code Course Title Credit Hours 540201 Artificial Intelligence 3.0 540202 Artificial Intelligence Lab 1.5 540203 Compiler Design and Construction 3.0 540204 Compiler Design Lab 1.5 540205 Computer Graphics 3.0 540206 Computer Graphics Lab 1.5 540207 E-Commerce and Web Engineering 3.0 540208 E-Commerce and Web Engineering Lab 1.5 Total Credits in 7th Semester 18.0 Semester VIII Course Code Course Title Credit Hours Major Theory Courses 540209 Network and Information Security 3.0 540210 Network and Information Security Lab 1.5 540211 Information System Management 3.0 Project/Industry Attachment 540240 Project/Industry Attachment 6.0 Optional Course (any one) 3.0 540212 Simulation and Modeling 540214 Parallel and Distributed Systems 540216 Digital Signal Processing 540218 Digital Image Processing 540220 Multimedia 540222 Pattern Recognition 540224 Design and Analysis of VLSI Systems 540226 Micro-controller and Embedded System 540228 Cyber Law and Computer Forensic 540230 Natural Language Processing 540232 System Analysis and Design 540234 Optical Fiber Communication 540236 Human Computer Interaction 540238 Graph Theory Page 2 of 18 Optional Course -
Color Combinations and Contrasts
COLOR COMBINATIONS AND CONTRASTS Full Value Color Combinations Above are 18 color combinations tested for visibility, using primary and secondary colors, of full hue and value. Visibility is ranked in the sequence shown, with 1 being the most visible and 18 being the least visible. Readability It is essential that outdoor designs are easy to read. Choose colors with high contrast in both hue and value. Contrasting colors are viewed well from great distances, while colors with low contrast will blend together and obscure a message. In fact, research demonstrates that high color contrast can improve outdoor advertising recall by 38 percent. A standard color wheel clearly illustrates the importance of contrast, hue and value. Opposite colors on the wheel are complementary. An example is red and green (as shown above). They respresent a good contrast in hue, but their values are similar. It is difficult for the human eye to process the wavelength variations associated with complementary colors. Therefore, a quivering or optical distortion is sometimes detected when two complemen- tary colors are used in tandem. Adjacent colors, such as blue and green, make especially poor combinations since their contrast is similar in both hue and value. As a result, adjacent colors create contrast that is hard to discern. Alternating colors, such as blue and yellow, produce the best combinations since they have good contrast in both hue and value. Black contrasts well with any color of light value and white is a good contrast with colors of dark value. For example, yellow and black are dissimilar in the contrast of both hue and value. -
ALL ABOUT COLOR March 2020 USA Version CONTENTS CHAPTER 1
ALL ABOUT COLOR March 2020 USA Version CONTENTS CHAPTER 1 WHO IS GOLDWELL CHAPTER 1 | WHO IS GOLDWELL | 4 WHO IS GOLDWELL 1948 1956 1970 1971 1976 FOUNDED BY SPRÜHGOLD OXYCUR TOP MODEL AIR FOAMED HANS ERICH DOTTER HAIRSPRAY PLATIN BLEACHING TOPCHIC PERMANENT PERM POWDER HAIR COLOR Focusing on hairdressers as business partners, Dotter launched the first Goldwell product: Goldwell Ideal, the innovative cold perm, which was to be followed by a never-ending flow of innovations. CHAPTER 1 | WHO IS GOLDWELL | 5 1978 1986 2001 2008 2009 2010 TOPCHIC COLORANCE ELUMEN DUALSENSES SILKLIFT STYLESIGN PERMANENT HAIR COLOR DEMI-PERMANENT NON-OXIDATIVE INSTANT SOLUTIONS HIGH PERFORMANCE FROM STYLISTS DEPOT SYSTEM HAIR COLOR HAIR COLOR HAIR CARE LIGHTENER FOR STYLISTS CHAPTER 1 | WHO IS GOLDWELL | 6 2012 2013 2015 2016 2018 NECTAYA KERASILK SILKLIFT CONTROL KERASILK COLOR SYSTEM AMMONIA-FREE KERATIN LIFT AND TONE LUXURY WITH @PURE PIGMENTS PERMANENT TREATMENT CONTROL HAIR CARE ELUMENATED COLOR HAIR COLOR ADDITIVES CHAPTER 2 WE THINK STYLIST CHAPTER 2 | WE THINK STYLIST | 8 WE THINK STYLIST BRAND STATEMENT We embrace your passion for beautiful hair. We believe that only together we can reach new heights by achieving creative excellence, outstanding client satisfaction and salon success. We do more than just understand you. We think like you. WE THINK STYLIST. CHAPTER 2 | WE THINK STYLIST | 9 GOLDWELL HAIR COLOR THE MOST INTELLIGENT AND COLOR CARING SYSTEM FOR CREATING AND MAINTAINING VIBRANT HEALTHY HAIR » Every day, we look at the salon experience through the eyes of a stylist – developing tools, color technology and innovations that fuel the creativity, streamline the work, and keep the clients looking and feeling fantastic. -
Adobe Photoshop 7.0 Design Professional
Adobe Photoshop CS Design Professional INCORPORATING COLOR TECHNIQUES Chapter Lessons Work with color to transform an image User the Color Picker and the Swatches palette Place a border around an image Blend colors using the Gradient Tool Add color to a grayscale image Use filters, opacity, and blending modes Match colors Incorporating Color Techniques Chapter D 2 Incorporating Color Techniques Using Color Develop an understanding of color theory and color terminology Identify how Photoshop measures, displays and prints color Learn which colors can be reproduced well and which ones cannot Incorporating Color Techniques Chapter D 3 Color Modes Photoshop displays and prints images using specific color modes Color mode or image mode determines how colors combine based on the number of channels in a color model Different color modes result in different levels of color detail and file size – CMYK color mode used for images in a full-color print brochure – RGB color mode used for images in web or e-mail to reduce file size while maintaining color integrity Incorporating Color Techniques Chapter D 4 Color Modes L*a*b Model – Based on human perception of color – Numeric values describe all colors seen by a person with normal vision Grayscale Model – Grayscale mode uses different shades of gray RGB (Red Green Blue) Mode used for online images – Assign intensity value to each pixel – Intensity values range from 0 (black) to 255 (white) for each of the RGB (red, green, blue) components in a color image • Bright red color has an R value of 246, a G value of 20, and a B value of 50. -
Visual Perceptual Skills
Super Duper® Handy Handouts!® Number 168 Guidelines for Identifying Visual Perceptual Problems in School-Age Children by Ann Stensaas, M.S., OTR/L We use our sense of sight to help us function in the world around us. It helps us figure out if something is near or far away, safe or threatening. Eyesight, also know as visual perception, refers to our ability to accurately identify and locate objects in our environment. Visual perception is an important component of vision that contributes to the way we see and interpret the world. What Is Visual Perception? Visual perception is our ability to process and organize visual information from the environment. This requires the integration of all of the body’s sensory experiences including sight, sound, touch, smell, balance, and movement. Most children are able to integrate these senses by the time they start school. This is important because approximately 75% of all classroom learning is visual. A child with even mild visual-perceptual difficulties will struggle with learning in the classroom and often in other areas of life. How Are Visual Perceptual Problems Diagnosed? A child with visual perceptual problems may be diagnosed with a visual processing disorder. He/she may be able to easily read an eye chart (acuity) but have difficulty organizing and making sense of visual information. In fact, many children with visual processing disorders have good acuity (i.e., 20/20 vision). A child with a visual-processing disorder may demonstrate difficulty discriminating between certain letters or numbers, putting together age-appropriate puzzles, or finding matching socks in a drawer.