DOCSLIB.ORG

Understanding Color Models: a Review 1 Noor A

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Understanding Color Models: a Review 1 Noor A VOL. 2, NO. 3, April 2012 ISSN 2225-7217 ARPN Journal of Science and Technology ©2011-2012. All rights reserved. http://www.ejournalofscience.org Understanding Color Models: A Review 1 Noor A. Ibraheem, 2 Mokhtar M. Hasan, 3 Rafiqul Z. Khan, 4 Pramod K. Mishra 1 Department of Computer Science, Faculty of Science, Aligarh Muslim University, Uttar Pradesh, India 1 E-Mail: [email protected] ABSTRACT Colors are important for human for communicating with the daily encountered objects as well as his species, these colors should be represented formally and numerically within a mathematical formula so it can be projected on device/ computer storage and applications, this mathematical representation is known as color model that can hold the color space, by the means of color’s primary components (Red, Green, and Blue) the computer can visualizes what the human does in hue and lightness. In this paper; a review of most popular color models are given with the explanation of the components, color system, and transformation formula for each other, application areas and usages are also included in this work with the classification of color models according to its dependence and independence on the hardware device used in specific application, a summary of the advantages and disadvantages of the color models are also demonstrated in this work. Keywords: Color model, color model applications, Luminance, Chromaticity. their corresponding advantages and disadvantages and their suitable application area. 1. INTRODUCTION One of the interesting fields that developed This paper is organized as follows; Section 2 instantaneous is digital image processing, which its explains common color model systems. Section 3 applications meet various life requirements such as demonstrates color models applications, classifications, communications, tracking signals, television, space, and their taxonomy. A summary of color model intelligent transportation systems etc. [1]. The main two advantages and disadvantages is given in Section 4. application areas of digital image processing as Finally conclusion is illustrated in Section 5. mentioned in [2]are to improve the picturing information for better human understanding as well as processing 2. COLOR MODELS image information for machine representation [2] which We can define the color model as the digital means to establishing a bridge between human-machine representation of possibly contained colors, a different communication by improving both ends. Using the color definition is found in [7] that defines it as the way that spectrum in image processing provides an interesting we can recognize color, where human can visualize color tool for objects recognition and extraction from the through its attributes such as; hue, and brightness [7]. scene[2] and to enable the extension of the domain space Color models is a system for measuring colors that can compared with gray images. Color model explains how perceived by human, and a process of combining the colors are represented and specifies the components different values as a set of primary colors [8]. Typically of color space accurately to learn how each color color models have three or four color components [5]. spectrum looks like[3].Color models are used for Different color spaces are available for different different applications such as; computer graphics [4], applications [7]. Color models can be divided into three image processing [4], TV broadcasting [4][6], and categories according to image processing applications; computer vision [3][4]. With knowledge of color models and different 1. Device-oriented color models: Also called color formats we can represent the color information of device dependent color models that relates and the input digital image acquired by a camera or scanner affected by the signal of the device [4], and the that can recognize three primary ingredient spectrums; resulted color affected by the tools used for red, green, and blue from the light beam [2] that displaying [7].These models are used widely in considered the primary components. The properties that many applications that demand the color be used to distinguish different colors are brightness, hue, consistent with hardware tools used [4], and saturation; these parameters are classified into two examples are any hardware devices that used components; luminance (the brightness) and for human vision perception such as TV [1] and chrominance (hue and saturation), so each color is video system [6]. represented with two characteristic components 2. User-oriented color models: Considered as a luminance and chrominance [2] that are suitable for path that existed between the observer and the human interaction [4] since they can represent the device handles the color information[4], these human skin pigment regardless the lightning used. Color models enable the user to describe and model is a mathematical model that briefly convert the approximate what he percepts from presenting light color coordinates position into three color the color [4]. components [5] in the three dimensional space using 3. Device-independent color models: The color some mathematical functions [6].We have prepared a model not affected by the given device global review paper for different color models used as properties [4], and the same color will be well as the mathematical representation of each with resulted from the set of parameters without any 265 VOL. 2, NO. 3, April 2012 ISSN 2225-7217 ARPN Journal of Science and Technology ©2011-2012. All rights reserved. http://www.ejournalofscience.org consideration for the devices performance and as CIE color space; XYZ is created by international [7].This type of color models are useful in commission on illumination 1931[5].These models are network transmission information so that the created manually with the help of human judgment visual data has to traverse through different ability of visualization and appearances matching, and hardware devices [4]. the chosen colorimetry is based on this matching procedure[4].Figure 2(A) explains the color matching Table 1: Illustrates the distribution of different function. The shapes of the sensitivity curves parameters color models according to latter mentioned X, Y and Z are measured with some plausible accuracy classification. [5].Mathematically speaking, the model can be described as luminance component Y along with two chromaticity Table 1: Color models classifications. coordinates X and Z[4], however, sometimes the XYZ Color Model Classifications color model is represented by its luminance parameter Y [5], furthermore, a normalized tristimulus (chromaticity Munsell Device independent coordinates) can be used as a representative for such model and calculated as in (1) and (2)(denoted by small RGB, CMY(K) Device dependent case xyz). YIQ,YUV, YCbCr Device dependent User oriented-Device HSI, HSV, HSL dependent CIE XYZ, CIE L*U*V*, Device independent, color CIE L*a*b* metric 3. THE MUNSELL COLOR SPACE The earliest organization of color perception into color space was Munsell color model [4] created by Professor Albert H. Munsell[5], and most familiar device independent color space [9].Munsell color model represented as a cylindrical shape with three dimensions equals to value (lightness), hue, and saturation (color purity)[4][5], and it was the first model that isolates the three color components into disciplinary independent, regular, and three dimensional space[5]. The principal of equality spacing between the model components is the main idea of Munsell color model [4],these components are hue, value and chroma, the hue is represented by a circular shape broken down into ten sectors defined as; red, yellow-red, yellow, green-yellow, green, blue-green, blue, purple-blue, purple and red-purple [4] which means the hue range is [1, 10],the value divided into eleven sections refer to lightness (white) at value ten or darkness (black) at value zero[4] which means the range is [0, 10] and perpendiculars the Munsell color model,and the chromare presents the saturation of the corresponding Figure 1: Munsell color system [4]. Right side shows selected combination of each of hue and value the hues circle at value 5, chroma 6; along the vertical V parameters and its range is [0, 12] as seen in Figure 1. value from 0 to 10 [5]. 4. CIE COLOR MODELS This color models has different combination as listed below: ……………. (1) I) CIE XYZ color Model One of the first color space defined is CIE ………….... (2) XYZ, also referred as X, Y, and Ztristimulus functions, 266 VOL. 2, NO. 3, April 2012 ISSN 2225-7217 ARPN Journal of Science and Technology ©2011-2012. All rights reserved. http://www.ejournalofscience.org Since only two coordinates used for color match description, the model represented with on xy- plane and the implicitly evaluated as . III) CIE L*a*b* Color Space Also called CIELAB color model the second uniform color space derived from CIE XYZ space II) CIE L*u*v* Color Space The first uniform color space derived from CIE in1976, with white reference point [4]. L*a*b* color XYZ space with white reference point as shown in model determines the color depending on its position in Figure 3[4] where the white reference point (Xn, Yn,Zn) a3D color space [11], the L* components is the lightness is the linear RGB (1, 1, 1) values. L* represents the of the color (when L* =0 means black and when L* lightness, and u* and v* represent the correlates of =100 referred to while) and the chroma*(for positive chroma and hue [4]. The details of conversion from CIE values indicate red and for negative values indicate XYZtoCIEL*u*v and the inverse from CIEL*u*v to CIE green) and the hue b* (positive values refer to yellow XYZ color models are available in reference [4]. while negative values refer to blue) [4] as illustrated in Figure 4. CIELAB is device independent and considered very important for desktop color [10].The details of conversion from CIE XYZ to CIEL*a*b*and the inverse from CIEL*a*b*to CIE XYZ color models are available in reference [4].
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]
  • Image Processing Terms Used in This Section Working with Different Screen Bit Describes How to Determine the Screen Bit Depth of Your Depths (P
    13 Color This section describes the toolbox functions that help you work with color image data. Note that “color” includes shades of gray; therefore much of the discussion in this chapter applies to grayscale images as well as color images. Topics covered include Terminology (p. 13-2) Provides definitions of image processing terms used in this section Working with Different Screen Bit Describes how to determine the screen bit depth of your Depths (p. 13-3) system and provides recommendations if you can change the bit depth Reducing the Number of Colors in an Describes how to use imapprox and rgb2ind to reduce the Image (p. 13-6) number of colors in an image, including information about dithering Converting to Other Color Spaces Defines the concept of image color space and describes (p. 13-15) how to convert images between color spaces 13 Color Terminology An understanding of the following terms will help you to use this chapter. Terms Definitions Approximation The method by which the software chooses replacement colors in the event that direct matches cannot be found. The methods of approximation discussed in this chapter are colormap mapping, uniform quantization, and minimum variance quantization. Indexed image An image whose pixel values are direct indices into an RGB colormap. In MATLAB, an indexed image is represented by an array of class uint8, uint16, or double. The colormap is always an m-by-3 array of class double. We often use the variable name X to represent an indexed image in memory, and map to represent the colormap. Intensity image An image consisting of intensity (grayscale) values.
    [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]
  • Cielab Color Space
    Gernot Hoffmann CIELab Color Space Contents . Introduction 2 2. Formulas 4 3. Primaries and Matrices 0 4. Gamut Restrictions and Tests 5. Inverse Gamma Correction 2 6. CIE L*=50 3 7. NTSC L*=50 4 8. sRGB L*=/0/.../90/99 5 9. AdobeRGB L*=0/.../90 26 0. ProPhotoRGB L*=0/.../90 35 . 3D Views 44 2. Linear and Standard Nonlinear CIELab 47 3. Human Gamut in CIELab 48 4. Low Chromaticity 49 5. sRGB L*=50 with RGB Numbers 50 6. PostScript Kernels 5 7. Mapping CIELab to xyY 56 8. Number of Different Colors 59 9. HLS-Hue for sRGB in CIELab 60 20. References 62 1.1 Introduction CIE XYZ is an absolute color space (not device dependent). Each visible color has non-negative coordinates X,Y,Z. CIE xyY, the horseshoe diagram as shown below, is a perspective projection of XYZ coordinates onto a plane xy. The luminance is missing. CIELab is a nonlinear transformation of XYZ into coordinates L*,a*,b*. The gamut for any RGB color system is a triangle in the CIE xyY chromaticity diagram, here shown for the CIE primaries, the NTSC primaries, the Rec.709 primaries (which are also valid for sRGB and therefore for many PC monitors) and the non-physical working space ProPhotoRGB. The white points are individually defined for the color spaces. The CIELab color space was intended for equal perceptual differences for equal chan- ges in the coordinates L*,a* and b*. Color differences deltaE are defined as Euclidian distances in CIELab. This document shows color charts in CIELab for several RGB color spaces.
    [Show full text]
  • Pale Intrusions Into Blue: the Development of a Color Hannah Rose Mendoza
    Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2004 Pale Intrusions into Blue: The Development of a Color Hannah Rose Mendoza Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY SCHOOL OF VISUAL ARTS AND DANCE PALE INTRUSIONS INTO BLUE: THE DEVELOPMENT OF A COLOR By HANNAH ROSE MENDOZA A Thesis submitted to the Department of Interior Design in partial fulfillment of the requirements for the degree of Master of Fine Arts Degree Awarded: Fall Semester, 2004 The members of the Committee approve the thesis of Hannah Rose Mendoza defended on October 21, 2004. _________________________ Lisa Waxman Professor Directing Thesis _________________________ Peter Munton Committee Member _________________________ Ricardo Navarro Committee Member Approved: ______________________________________ Eric Wiedegreen, Chair, Department of Interior Design ______________________________________ Sally Mcrorie, Dean, School of Visual Arts & Dance The Office of Graduate Studies has verified and approved the above named committee members. ii To Pepe, te amo y gracias. iii ACKNOWLEDGMENTS I want to express my gratitude to Lisa Waxman for her unflagging enthusiasm and sharp attention to detail. I also wish to thank the other members of my committee, Peter Munton and Rick Navarro for taking the time to read my thesis and offer a very helpful critique. I want to acknowledge the support received from my Mom and Dad, whose faith in me helped me get through this. Finally, I want to thank my son Jack, who despite being born as my thesis was nearing completion, saw fit to spit up on the manuscript only once.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Real-Time Supervised Detection of Pink Areas in Dermoscopic Images of Melanoma: Importance of Color Shades, Texture and Location
    Missouri University of Science and Technology Scholars' Mine Electrical and Computer Engineering Faculty Research & Creative Works Electrical and Computer Engineering 01 Nov 2015 Real-Time Supervised Detection of Pink Areas in Dermoscopic Images of Melanoma: Importance of Color Shades, Texture and Location Ravneet Kaur P. P. Albano Justin G. Cole Jason R. Hagerty et. al. For a complete list of authors, see https://scholarsmine.mst.edu/ele_comeng_facwork/3045 Follow this and additional works at: https://scholarsmine.mst.edu/ele_comeng_facwork Part of the Chemistry Commons, and the Electrical and Computer Engineering Commons Recommended Citation R. Kaur et al., "Real-Time Supervised Detection of Pink Areas in Dermoscopic Images of Melanoma: Importance of Color Shades, Texture and Location," Skin Research and Technology, vol. 21, no. 4, pp. 466-473, John Wiley & Sons, Nov 2015. The definitive version is available at https://doi.org/10.1111/srt.12216 This Article - Journal is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Electrical and Computer Engineering Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. Published in final edited form as: Skin Res Technol. 2015 November ; 21(4): 466–473. doi:10.1111/srt.12216. Real-time Supervised Detection of Pink Areas in Dermoscopic Images of Melanoma: Importance of Color Shades, Texture and Location Ravneet Kaur, MS, Department of Electrical and Computer Engineering, Southern Illinois University Edwardsville, Campus Box 1801, Edwardsville, IL 62026-1801, Telephone: 618-210-6223, [email protected] Peter P.
    [Show full text]
  • Tracking and Automation of Images by Colour Based
    Vol 11, Issue 8,August/ 2020 ISSN NO: 0377-9254 TRACKING AND AUTOMATION OF IMAGES BY COLOUR BASED PROCESSING N Alekhya 1, K Venkanna Naidu 2 and M.SunilKumar 3 1PG student, D.N.R College of Engineering, ECE, JNTUK, INDIA 2 Associate Professor D.N.R College of Engineering, ECE, JNTUK, INDIA 3Assistant Professor Sir CRR College of Engineering , EEE, JNTUK, INDIA [email protected], [email protected] ,[email protected] Abstract— Now a day all application sectors are mostly image analysis involves maneuver the moving for the automation processing and image data to conclude exactly the information sensing . for example image processing in compulsory to help to answer a computer imaging medical field ,in industrial process lines , object problem. detection and Ranging application, satellite Digital image processing methods stems from two imaging Processing ,Military imaging etc, In principal application areas: improvement of each and every application area the raw images pictorial information for human interpretation, and are to be captured and to be processed for processing of image data for tasks such as storage, human visual inspection or digital image transmission, and extraction of pictorial processing systems. Automation applications In information this proposed system the video is converted into The remaining paper is structured as follows. frames and then it is get divided into sub bands Section 2 deals with the existing method of Image and then background is get subtracted, then the Processing. Section 3 deals with the proposed object is get identified and then it is tracked in method of Image Processing. Section 4 deals the the framed from the video .This work presents a results and discussions.
    [Show full text]
  • Accurately Reproducing Pantone Colors on Digital Presses
    Accurately Reproducing Pantone Colors on Digital Presses By Anne Howard Graphic Communication Department College of Liberal Arts California Polytechnic State University June 2012 Abstract Anne Howard Graphic Communication Department, June 2012 Advisor: Dr. Xiaoying Rong The purpose of this study was to find out how accurately digital presses reproduce Pantone spot colors. The Pantone Matching System is a printing industry standard for spot colors. Because digital printing is becoming more popular, this study was intended to help designers decide on whether they should print Pantone colors on digital presses and expect to see similar colors on paper as they do on a computer monitor. This study investigated how a Xerox DocuColor 2060, Ricoh Pro C900s, and a Konica Minolta bizhub Press C8000 with default settings could print 45 Pantone colors from the Uncoated Solid color book with only the use of cyan, magenta, yellow and black toner. After creating a profile with a GRACoL target sheet, the 45 colors were printed again, measured and compared to the original Pantone Swatch book. Results from this study showed that the profile helped correct the DocuColor color output, however, the Konica Minolta and Ricoh color outputs generally produced the same as they did without the profile. The Konica Minolta and Ricoh have much newer versions of the EFI Fiery RIPs than the DocuColor so they are more likely to interpret Pantone colors the same way as when a profile is used. If printers are using newer presses, they should expect to see consistent color output of Pantone colors with or without profiles when using default settings.
    [Show full text]
  • 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.
    [Show full text]