By Stephen Westland, Caterina Ripamonti
Providing a practical, problem-based method of color physics, this title describes the main concerns encountered in glossy color engineering, together with effective illustration of color details, fourier research of reflectance spectra and complex colorimetric computation. Emphasis is put on the sensible functions instead of the concepts themselves, with fabric dependent round key issues, equivalent to color calibration of visible screens, machine recipe prediction and types for colour-appearance prediction.
Each subject is thoroughly brought at 3 degrees to augment pupil figuring out. first of all, theoretical principles and history details are mentioned, factors of mathematical options then stick to and at last sensible ideas are offered utilizing MATLAB.* incorporates a compendium of equations and numerical facts required by means of the fashionable color and imaging scientist.* a variety of examples of ideas and algorithms for a wide-range of computational difficulties in color science.* presents instance scripts utilizing the MATLAB programming language.
This textual content is a must-have for scholars taking classes in color technology, color chemistry and color physics in addition to technicians and researchers operating within the area.
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Additional info for Computational Colour Science using MATLAB
2 Reflectance interpolation The CIE recommended method for interpolation of reﬂectance spectra is to use cubic polynomial interpolation using two points either side of the wavelength to be evaluated. If the reﬂectance spectrum is available at intervals of 20 nm, then the value of reﬂectance at 470 nm, for example, would be calculated using a cubic polynomial ﬁtted through the reﬂectance at 440, 460, 480 and 500 nm. The function pinterp takes an N-dimensional reﬂectance vector and applies piecewise cubic polynomial interpolation to generate an additional point between each pair of points in the vector.
This procedure is known as Neville’s algorithm. 1 for a large number of reﬂectance spectra illustrate that the cubic Lagrange polynomial is almost certainly adequate for any practical interpolation of reﬂectance spectra. This may not be the case for the interpolation of spectral power distributions of light sources, however, since these can be spiky. For users who may wish to write code in languages other than MATLAB we provide a function called pinterp that performs interpolation by cubic Langrange polynomials and that contains no MATLAB library calls other than the backslash operator.
3 for a brief review) originally deﬁned the tristimulus values in terms of an integration over wavelength l, thus: R X ¼ k EðlÞPðlÞxðlÞdl, R Y ¼ k EðlÞPðlÞyðlÞdl, ð4:1Þ R Z ¼ k EðlÞPðlÞzðlÞdl, Computational Colour Science Using MATLAB. By Stephen Westland and Caterina Ripamonti. & 2004 John Wiley & Sons, Ltd: ISBN 0 470 84562 7 28 COMPUTING CIE TRISTIMULUS VALUES where E(l) is the relative spectral power distribution of an illuminant, x(l), y(l) and z(l) are the colour-matching functions for the CIE 1931 or 1964 standard observers, P(l) is the R spectral reﬂectance of a surface and k is a normalizing factor given by 100/ E(l)y(l)dl.