Unique Hues & Principal Hues
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Basic Color I
Basic Color I Up to this point we have explored and /or reviewed 4 several basic visual elements that will help develop a solid foundation for a Language of Painting. With a strong focus on the dynamics of paint application, we have connected simple dots with confident lines, con- figured lines into a wide array of shapes, and applied contrasting pigments in concert with varied pressures to yield seamless gradations of value. However, the previous chapter incorporated a new element that many find to be one of the most powerful tools for the creative endeavor-- color. The Painted Pressure Scale chapter, had us grow our palette from the sparse black and white that we have started with to include three colors: Red, Yellow and Blue. It is our hope that your experience generated a good number of questions from this inaugural color use. With this chapter we will take some first steps towards answering those questions. We will also revisit some of the color concepts you may already hold and introduce you to some of the more advanced methods for identifying, using and understanding this brilliant aspect of the artist’s salvo. On any initial investigation into color we are faced with a robust vocabulary of terms and concepts that may leave us somewhat confused. Like the many other aspects of our curriculum, we make a strong effort to simplify all of this. We will endeavor to make use of what you may have learned in the past and offer options for you to integrate color in the manner you wish into your creative process efficiently and effectively. -
Development of a Methodology for Analyzing the Color Content of a Selected Group of Printed Color Analysis Systems
AN ABSTRACT OF THE THESIS OF Edith E. Collin for the degree of Master of Sciencein Clothing, Textiles and Related Arts presented on April 7, 1986. Title: Development of a Methodology for Analyzing theColor Content of a Selected Group of Printed Color Analysis Systems Redacted for Privacy Abstract approved: Ardis Koester The purpose of this study was to develop amethodology to compare the color choice recommendationsfor each personal color analysis category identified by the authorsof selected publications. The procedure used included: (1) identification of publications with color analysis systemsdirected toward female clientele; (2) comparison of number and names of categoriesused; (3) identification, by use of Munsell colornotations, the visual and written color recommendations ascribed toeach category; and (4) comparison of the publications on the basisof: (a) number and names of categories; (b) numberof color recommendations in each category; (c) range of hue value and chroma presented;(d) comparison of visual and written color recommendations by categoryand author. With the exception of comparison of publications onthe basis of written color recommendations, all components of themethodology were successful. Comparison of the publications used in development ofthe methodology revealed that: 1. The majority of authors use the seasonal category system. 2. The number of color recommendations per category was quite consistent within a publication but varied widely among authors. 3. There were few similarities in color recommendations even among authors using the same name categories. 4. There was poor agreement between written and visual color recommendations within all color categories. 5. There was no discernable theoretical basis for the color recommendations presented by any author included in this study. -
Color Space Conversion
L Technical Color Space Information Conversion The role of color space conversion in the process of scanning and reproduc- ing a color image begins the moment an image is captured by a scanner and continues through the point at which it is output on film. For the purpose of this article we will discuss conversions between three types of color systems: RGB, CIE, and CMYK. The RGB (Red, Green, & Blue) and CMYK (Cyan, Magenta, Yellow, & Black) color models have been described in greater detail in the Linotype-Hell Technical information piece entitled Color in Printing. For some background information on the CIE (Commission Internationale de l’Eclairage), please refer to Color Spaces and PostScript Level 2. CIE as a reference color space Most scanners acquire color in the form of RGB data. The majority of non- photographic printing methods employ CMYK inks or toners. In a closed sys- tem, where the characteristics of both the scanner and the printing method are well-defined, conversions may be made from RGB to CMYK through tables that maintain a reasonable level of color consistency. The process becomes somewhat more difficult as you add monitors, other scanners, proofing devices or printing processes that have different characteristics. It becomes critical to have a consistent yardstick, if you will, a means of con- verting between different color systems (and back again) without a loss in color fidelity. This is what CIE provides. Let’s start with some background on CIE, and then we will look at how it can be applied in an open system. Measuring color Light reflected off of, or transmitted through a colored object can be mea- sured by the wavelengths of light that are reflected or transmitted. -
Color Measurement1 Agr1c Ü8 ,
I A^w /\PK4 1946 USDA COLOR MEASUREMENT1 AGR1C ü8 , ,. 2001 DEC-1 f=> 7=50 AndA ItsT ApplicationA rL '"NT SERIAL Í to the Grading of Agricultural Products A HANDBOOK ON THE METHOD OF DISK COLORIMETRY ui By S3 DOROTHY NICKERSON, Color Technologist, Producdon and Marketing Administration 50! es tt^iSi as U. S. DEPARTMENT OF AGRICULTURE Miscellaneous Publication 580 March 1946 CONTENTS Page Introduction 1 Color-grading problems 1 Color charts in grading work 2 Transparent-color standards in grading work 3 Standards need measuring 4 Several methods of expressing results of color measurement 5 I.C.I, method of color notation 6 Homogeneous-heterogeneous method of color notation 6 Munsell method of color notation 7 Relation between methods 9 Disk colorimetry 10 Early method 22 Present method 22 Instruments 23 Choice of disks 25 Conversion to Munsell notation 37 Application of disk colorimetry to grading problems 38 Sample preparation 38 Preparation of conversion data 40 Applications of Munsell notations in related problems 45 The Kelly mask method for color matching 47 Standard names for colors 48 A.S.A. standard for the specification and description of color 50 Color-tolerance specifications 52 Artificial daylighting for grading work 53 Color-vision testing 59 Literature cited 61 666177—46- COLOR MEASUREMENT And Its Application to the Grading of Agricultural Products By DOROTHY NICKERSON, color technologist Production and Marketing Administration INTRODUCTION cotton, hay, butter, cheese, eggs, fruits and vegetables (fresh, canned, frozen, and dried), honey, tobacco, In the 16 years since publication of the disk method 3 1 cereal grains, meats, and rosin. -
ARCA (Automatic Recognition of Color for Archaeology): a Desktop Application for Munsell Estimation
ARCA (Automatic Recognition of Color for Archaeology): a Desktop Application for Munsell Estimation Filippo L.M. Milotta1;2, Filippo Stanco1, and Davide Tanasi2 1 Image Processing Laboratory (IPLab) Department of Mathematics and Computer Science University of Catania, Italy fmilotta, [email protected] 2 Center for Virtualization and Applied Spatial Technologies (CVAST) Department of History University of South Florida, Florida [email protected], [email protected] Abstract. Archaeologists are used to employing the Munsell Soil Charts on cultural heritage sites to identify colors of soils and retrieved artifacts. The standard practice of Munsell estimation exploits the Soil Charts by visual means. This procedure is error prone, time consuming and very subjective. To obtain an accurate estimation the process should be re- peated multiple times and possibly by other users, since colors might not be perceived uniformly by different people. Hence, a method for objective and automatic Munsell estimation would be a valuable asset to the field of archaeology. In this work we present ARCA: Automatic Recognition of Color for Archaeology, a desktop application for Munsell estimation. The following pipeline for Munsell estimation aimed towards archaeologists has been proposed: image acquisition of specimens, manual sampling of the image in the ARCA desktop application, automatic Munsell estima- tion of the sampled points and creation of a sampling report. A dataset, called ARCA108, consisting of 22; 848 samples has been gathered, in an unconstrained environment, and evaluated with respect to the Munsell Soil Charts. Experimental results are reported to define the best con- figuration that should be used in the acquisition phase. -
On the Nature of Unique Hues
Reprinted from Dickinson, C., Murray, 1. and Carden, D. (Eds) 'John Dalton's Colour Vision Legacy', 1997, Taylor & Francis, London On the Nature of Unique Hues J. D. Mollon and Gabriele Jordan 7.1 .l INTRODUCTION There exist four colours, the Urfarben of Hering, that appear phenomenologically un- mixed. The special status of these 'unique hues' remains one of the central mysteries of colour science. In Hering's Opponent Colour Theory, unique red and green are the colours seen when the yellow-blue process is in equilibrium and when the red-green process is polarised in one direction or the other. Similarly unique yellow and blue are seen when the red-green process is in equilibrium and when the yellow-blue process is polarised in one direction or the other. Most observers judge that other hues, such as orange or cyan, partake of the qualities of two of the Urfarben. Under normal viewing conditions, however, we never experience mixtures of the two components of an opponent pair, that is, we do not experience reddish greens or yellowish blues (Hering, 1878). These observations are paradigmatic examples of what Brindley (1960) called Class B observations: the subject is asked to describe the quality of his private sensations. They differ from Class A observations, in which the subject is required only to report the identity or non- identity of the sensations evoked by different stimuli. We may add that they also differ from the performance measures (latency; frequency of error; magnitude of error) that treat the subject as an information-processingsystem and have been increasingly used in visual science since 1960. -
Colorwatch: Color Perceptual Spatial Tactile Interface for People with Visual Impairments
electronics Article ColorWatch: Color Perceptual Spatial Tactile Interface for People with Visual Impairments Muhammad Shahid Jabbar 1 , Chung-Heon Lee 1 and Jun Dong Cho 1,2,* 1 Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea; [email protected] (M.S.J.); [email protected] (C.-H.L.) 2 Department of Human ICT Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea * Correspondence: [email protected] Abstract: Tactile perception enables people with visual impairments (PVI) to engage with artworks and real-life objects at a deeper abstraction level. The development of tactile and multi-sensory assistive technologies has expanded their opportunities to appreciate visual arts. We have developed a tactile interface based on the proposed concept design under considerations of PVI tactile actuation, color perception, and learnability. The proposed interface automatically translates reference colors into spatial tactile patterns. A range of achromatic colors and six prominent basic colors with three levels of chroma and values are considered for the cross-modular association. In addition, an analog tactile color watch design has been proposed. This scheme enables PVI to explore artwork or real-life object color by identifying the reference colors through a color sensor and translating them to the tactile interface. The color identification tests using this scheme on the developed prototype exhibit good recognition accuracy. The workload assessment and usability evaluation for PVI demonstrate promising results. This suggest that the proposed scheme is appropriate for tactile color exploration. Citation: Jabbar, M.S.; Lee, C.-H.; Keywords: color identification; tactile perception; cross modular association; universal design; Cho, J.D. -
Appendix G: the Pantone “Our Color Wheel” Compared to the Chromaticity Diagram (2016) 1
Appendix G - 1 Appendix G: The Pantone “Our color Wheel” compared to the Chromaticity Diagram (2016) 1 There is considerable interest in the conversion of Pantone identified color numbers to other numbers within the CIE and ISO Standards. Unfortunately, most of these Standards are not based on any theoretical foundation and have evolved since the late 1920's based on empirical relationships agreed to by committees. As a general rule, these Standards have all assumed that Grassman’s Law of linearity in the visual realm. Unfortunately, this fundamental assumption is not appropriate and has never been confirmed. The visual system of all biological neural systems rely upon logarithmic summing and differencing. A particular goal has been to define precisely the border between colors occurring in the local language and vernacular. An example is the border between yellow and orange. Because of the logarithmic summations used in the neural circuits of the eye and the positions of perceived yellow and orange relative to the photoreceptors of the eye, defining the transition wavelength between these two colors is particularly acute.The perceived response is particularly sensitive to stimulus intensity in the spectral region from 560 to about 580 nanometers. This Appendix relies upon the Chromaticity Diagram (2016) developed within this work. It has previously been described as The New Chromaticity Diagram, or the New Chromaticity Diagram of Research. It is in fact a foundation document that is theoretically supportable and in turn supports a wide variety of less well founded Hering, Munsell, and various RGB and CMYK representations of the human visual spectrum. -
COLOUR11. Phenomenology
In a center- surround cell of any kind, you have two receptive fields, one the small center region, the other the surround region. In a chromatic center-surround field, each in innervated by one class of receptor, and — their signals are antagonistic + _ — If you took the input — + from 2 different cones, each with the same receptive field, and connected them to single neuron, you would have a neuron that signaled wavelength contrast (but not across any space.) — + — + _ — With a blue light in the surround, the surround provides inhibition. 100% blue surround inhibition; no center excitation. Entire cell = No firing (-100%) — + _ — With a blue light in the center, there is no excitation. Entire cell = no change to base rate — + _ — With a sky blue light in the surround, the surround provides inhibition (55%). With the sky blue light in the center, the center provides 10 % excitation Entire cell = - 40% — + _ — If only the surround is illuminated, then the surround provides inhibition (30%) Entire Cell: -30%. — + _ — If only the center is illuminated, then the center is excited by 30%. Entire Cell: +30%. — + _ — 1:1 ratio of excitation The Null Point: if both the center and the surround were illuminated by light at this wavelength, there would be no change to the base firing rate of the cell. Entire cell = Base rate firing — + _ — With green light in the center; the center is excited (45%). There is no light on the surround; so the surround provides no inhibition. Entire cell = +45% — + _ — With green light in surround inhibits the surround 10% There is no light on the center; so the center provides no excitation. -
Major Color Order Systems and Their Psychophysical Structure
Chapter 7 Major Color Order Systems and Their Psychophysical Structure In this chapter only the Munsell, OSA-UCS, and Swedish NCS systems will be discussed. The former two are the most important attempts to create psy- chologically uniform systems, the latter uses the presumed natural approach of Hering (see Chapter 2) to create a color order system, having a regular structure, but not one uniform in terms of size of perceived differences. There are several other newer color order systems extant, but they neither make claims for uniformity nor for regularity according to new, significant psycho- logical attributes. The issue of viewing conditions for these systems has been attended to in different ways.As discussed previously,the Munsell system is illustrated as if the chips at each value level would be viewed against an achromatic surround of the same value.Chips of two adjacent value levels are illustrated as if viewed against an achromatic surround of intermediate value. The actual atlas displays the chips on white paper (historically of various degrees of whiteness), thus result- ing in distortions of the value scale, particularly at lower values.The OSA-UCS system is defined for an achromatic surround of luminous reflectance Y = 30 (L = 0).The atlas samples are in transparent jackets.NCS,finally,has been estab- lished against an immediate achromatic surround of Y = 78 in a light booth painted with an achromatic gray of Y = 54.The atlas displays samples on white paper. Both latter systems only result in the intended color experiences when viewed against the appropriate surrounds. Munsell and NCS are defined for Color Space and Its Divisions: Color Order from Antiquity to the Present, by Rolf G. -
PRECISE COLOR COMMUNICATION COLOR CONTROL from PERCEPTION to INSTRUMENTATION Knowing Color
PRECISE COLOR COMMUNICATION COLOR CONTROL FROM PERCEPTION TO INSTRUMENTATION Knowing color. Knowing by color. In any environment, color attracts attention. An infinite number of colors surround us in our everyday lives. We all take color pretty much for granted, but it has a wide range of roles in our daily lives: not only does it influence our tastes in food and other purchases, the color of a person’s face can also tell us about that person’s health. Even though colors affect us so much and their importance continues to grow, our knowledge of color and its control is often insufficient, leading to a variety of problems in deciding product color or in business transactions involving color. Since judgement is often performed according to a person’s impression or experience, it is impossible for everyone to visually control color accurately using common, uniform standards. Is there a way in which we can express a given color* accurately, describe that color to another person, and have that person correctly reproduce the color we perceive? How can color communication between all fields of industry and study be performed smoothly? Clearly, we need more information and knowledge about color. *In this booklet, color will be used as referring to the color of an object. Contents PART I Why does an apple look red? ········································································································4 Human beings can perceive specific wavelengths as colors. ························································6 What color is this apple ? ··············································································································8 Two red balls. How would you describe the differences between their colors to someone? ·······0 Hue. Lightness. Saturation. The world of color is a mixture of these three attributes. -
Reflectance Measurements of Pigmented Colorants
Reflectance Measurements of Pigmented Colorants Jeffrey B. Budsberg Donald P. Greenberg Stephen R. Marschner Technical Report PCG-06-02 Cornell Program of Computer Graphics September 6, 2006 In this report, we present the results of an experimental study on the appearance of artists’ paint over time. Paint samples were handmade to ensure material quality, using various pigment colorants and adhesive binding media. We present the results of our diffuse reflectance measurements, which show very significant perceptual differences in two different domains: how the appearance of paint changes over time; and how the appearance of one pigmented colorant varies when dispersed in different materials. Acknowledgements This work would not have been possible without the contributions and support from many individuals. The authors would like to thank Stan Taft for his commentary in the early stages of this research. Fabio Pellacini proved to be a great resource for hashing through ideas, as well as aiding in the implementation of the rendering code in graphics hardware. James Ferwerda was very helpful in the discussion of color science and visual perception. We thank Steve Westin for assisting in the setup of the equipment in the light measurement laboratory and Victor Kord for lending his expertise on artists’ materials. This research was supported by the Program of Computer Graphics, the Depart- ment of Architecture and the National Science Foundation ITR/AP 0205438. i Table of Contents 1 Background 1 1.1 Introduction . 1 1.2 Color Spaces . 3 2 Results 5 2.1 Reflectance Data . 5 2.2 Effect of Binding Media . 30 2.3 Effect of Time .