2008
DOI: 10.1002/col.20458
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Reconstruction of reflectance data by modification of Berns' Gaussian method

Abstract: Berns' method for the synthesis of spectral reflectance curve from the tristimulus color coordinates is modified. Firstly, the Gaussian bell shape red primary is replaced with a sigmoidal one to solve the dissimilarity between the spectral curves at the end region of spectrum. Secondly, three predetermined Gaussian primaries used in the original Berns' method are replaced by the adaptive ones which their half-height bandwidths vary with the tristimulus values of the desired color. The mentioned modifications a… Show more

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Cited by 13 publications
(8 citation statements)
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“…There are many ways to generate reconstructed spectral distributions from target tristimulus values. [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] The solution is not unique; there is an entire "metameric suite" of spectral distributions that share common tristimulus values. The outcome of each reconstruction algorithm differs according to the assumptions and restrictions imposed on the reconstruction.…”
Section: Spectral Reconstruction Computationmentioning
confidence: 99%
See 1 more Smart Citation
“…There are many ways to generate reconstructed spectral distributions from target tristimulus values. [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] The solution is not unique; there is an entire "metameric suite" of spectral distributions that share common tristimulus values. The outcome of each reconstruction algorithm differs according to the assumptions and restrictions imposed on the reconstruction.…”
Section: Spectral Reconstruction Computationmentioning
confidence: 99%
“…Spectral reconstruction is the process of generating a distribution (eg, reflectance, power, etc) over a wavelength (or frequency) domain, given only a three‐dimensional representation of the color, such as tristimulus values referenced to some illuminant. There are many ways to generate reconstructed spectral distributions from target tristimulus values . The solution is not unique; there is an entire “metameric suite” of spectral distributions that share common tristimulus values.…”
Section: Spectral Reconstruction Computationmentioning
confidence: 99%
“…The goal of generating a reflectance distribution over the visible portion of the spectrum from a specified three‐dimensional color specifier (eg, tristimulus values) has been the subject of many investigations . The solution is not unique; there is an entire “metameric suite” of spectral reflectance functions that share a common triplet of tristimulus values for a particular illuminant .…”
Section: Introductionmentioning
confidence: 99%
“…The transformation from spectral reflectance to colorimetric coordinates is many‐to‐one and therefore colorimetric data for objects cannot be unambiguously transformed back to spectral reflectance . Thus, different methods such as simulated annealing, simplex method, neural networks, application of subtractive and additive Gaussian primaries, linear methods, the Hawkyard method, the improved Hawkyard method, genetic algorithms, matrix R method, Principal Component Analysis (PCA), nonnegative matrix factorization, Independent Component Analysis, the lookup‐table method, and local linear interpolation method have been developed to extraction the reflectance spectrum from their corresponding colorimetric data …”
Section: Introductionmentioning
confidence: 99%
“…11 Thus, different methods such as simulated annealing, simplex method, neural networks, application of subtractive and additive Gaussian primaries, linear methods, the Hawkyard method, the improved Hawkyard method, genetic algorithms, matrix R method, Principal Component Analysis (PCA), nonnegative matrix factorization, Independent Component Analysis, the lookup-table method, and local linear interpolation method have been developed to extraction the reflectance spectrum from their corresponding colorimetric data. 1,2,5,8,10,[12][13][14][15][16][17][18][19][20][21][22][23] Among these methods, PCA is simple, linear, and nonparametric method and has been widely used to estimation of spectra. 8,[24][25][26][27] PCA is an important mathematical technique in color science, which has been used in data analysis, compression, and extraction of the data-based covariance matrix.…”
Section: Introductionmentioning
confidence: 99%