Tristimulus specification of the Munsell book of color from spectrophotometric measurements

Kelly, K.L.; Gibson, K. S.; Nickerson, Dorothy

doi:10.6028/jres.031.023

Cited by 17 publications

(15 citation statements)

References 5 publications

(5 reference statements)

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“…These charts are similar to figures 2 to 8 of Research Paper RP1549 [19] and include points corresponding to the measurements of the third intermediate hues [71, and they include also broken lines for each chroma and each hue step to facilitate linear interpolation between the plotted points.…”

Section: Munsell Book Notationsmentioning

confidence: 99%

“…Oomparison of these differences with those obtained by means of the spectrophotometer serves to disclose most of the important errors in chromaticity coordinates. If the comparison is made with a previously measured Munsell st,andard [7,19], the differences in chromaticitv coordinates, x and V, may be added to the known values for the standard, and chromaticity coordinates, x and y, found for the sample. This was done for each sample classified as strongly fluorescent.…”

Section: Chromaticity-difference Colorimetermentioning

confidence: 99%

“…where R. is the published luminous refleotance [7,19] for the Munsell standard evaluated from spectrophotometric measurement for perpendicular illumination. There is a correction to be applied to R2 found by this formula if the light reflected from the sample is not in the same state of polarization as that from the standard [23].…”

Section: Macbeth-martens Reflectometermentioning

confidence: 99%

“…The angular spreads of the illuminating and viewing directions are far too small to be equivalent to the desired 50° to 70° spread of the illuminating beams near a window. Each Munsell standard was backed by a white card or a black card to correspond to the backing used in its original spectrophotometric calibration [7,19].…”

Section: Chromaticity-difference Colorimetermentioning

confidence: 99%

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Spectrophotometric and colorimetric determination of the colors of the TCCA standard color cards

G¹,

Db²,

Hj³

1946

J. RES. NATL. BUR. STAN.

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The color c ards of the Textile Color Card Association of the United Stat es are widely used i n the textile and a lli ed industries and by many p rocuring agencies o f t h e Federal Government. The Textile Color Card Association issues both seasonal and standard color cards. The seasonal car ds p rovide a color-fo recastin g sery ice to textile manufacturers and promote color coord ination am ong t h e t rades; the standard cards present colors for which there is a popu la r and continuing demand. Most impor tant of the color cards is t he Standar d Color Card of Ameri ca, the current nint h ed ition containi ng 216 colors. Preeminent amon g t he m a ny special sets of color cards iss ued by t he Association for use of the Federa l Governm ent is t h e United States Army Color Card showing 22 official colors for the arms and services. T he specification of the colors of t he Ninth Edition Standard Color Card and the Un ited States Army Color Card h as been undertaken for the pur pose of correlating these textile standar ds with American War Standard Z44-1942 for the specification and description of color. T he 238 samples comprising these color cards have been examined by basic spectrophotometric and colorimetric procedures. From th is study t here have been foun d d ayli ght r eflectance, Y, ch romaticity coordinates (x,y) , Munsell renota.t ions, and I SCC-NBS color designations for these samples, as recommended by American W ar Stan dar d Z44-1942. As more than half of these texti le s tandards a re flu orescent, and as existing spect.rophotometers are not sui ted t o t h e evaluation of such samples, considerahle reliance has been p laced on q uantitative co lorimetri c an d p hotometric comparisons with t h e Munsell colo r standards, both by means of a chromati city-difference colorimet.er and by t he Martens p h otom eter.As a closing check, Munsell book notations of these textile standards h ave been obtained by a direct visual comparison with the color scales of t he Munsell Book of Color.

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Section: Munsell Book Notationsmentioning

confidence: 99%

Section: Chromaticity-difference Colorimetermentioning

confidence: 99%

Section: Macbeth-martens Reflectometermentioning

confidence: 99%

Section: Chromaticity-difference Colorimetermentioning

confidence: 99%

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Spectrophotometric and colorimetric determination of the colors of the TCCA standard color cards

G¹,

Db²,

Hj³

1946

J. RES. NATL. BUR. STAN.

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“…We choose a linear model for surfaces by performing principal components analysis on the data of Kelly et al [37,53] who measured the reectance functions of 462 Munsell papers. We then found the best tting model weights for each individual surface in the data set.…”

Section: Priorsmentioning

confidence: 99%

Bayesian decision theory, the maximum local mass estimate, and color constancy

Freeman¹,

Brainard

Proceedings of IEEE International Conference on Computer Vision

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Computational vision algorithms are often developed in a Bayesian framework. Two estimators are commonly used: maximum a posteriori (MAP), and minimum mean squared error (MMSE). We argue that neither is appropriate for perception problems. The MAP estimator makes insufficient use of structure in the posterior probability. The squared error penalty of the MMSE estimator does not reflect typical penalties. We apply this new estimator to color constancy. An unknown illuminant falls on surfaces of unknown colors. We seek to estimate both the illuminant spectrum and the surface spectra from photosensor responses which depend on the product of these unknown spectra. In simulations, we show that the MLM method performs better than the MAP estimator, and better than two standard color constancy algorithms. The MLM estimate may prove useful in other vision problems as well.IEEE Intl. Conf. on Computer Vision, Cambridge, MA, June, 1995 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. AbstractVision algorithms are often developed in a Bayesian framework. Two estimators are commonly used: maximum a posteriori (MAP), and minimum mean squared error (MMSE). We argue that neither is appropriate for perception problems. The MAP estimator makes insucient use of structure in the posterior probability. The squared error penalty of the MMSE estimator does not reect typical penalties.We describe a new estimator, which we call maximum local mass (MLM) [10,26,65], which integrates the local probability density. The MLM method is sensitive to local structure of the posterior probability, which MAP is not. The new method uses an optimality criterion that is appropriate for perception tasks: it nds the most probable approximately correct answer. For the case of low observation noise, we provide an ecient approximation.We apply this new estimator to color constancy. An unknown illuminant falls on surfaces of unknown colors. We seek to estimate both the illuminant spectrum and the surface spectra from photosensor responses which depend on the product of these unknown spectra. In simulations, we show that the MLM method performs better than the MAP estimator, and better than two standard color constancy algorithms. The MLM method may prove useful in other vision problems as well.

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Colorimetry and Spectrophotometry

Joslyn¹

1950

Methods in Food Analysis

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Tristimulus specification of the Munsell book of color from spectrophotometric measurements

Cited by 17 publications

References 5 publications

Spectrophotometric and colorimetric determination of the colors of the TCCA standard color cards

Spectrophotometric and colorimetric determination of the colors of the TCCA standard color cards

Bayesian decision theory, the maximum local mass estimate, and color constancy

Colorimetry and Spectrophotometry

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