By Stephen Westland, Caterina Ripamonti, Vien Cheung(auth.), Michael A. Kriss(eds.)
Computational color technological know-how utilizing MATLAB 2nd Edition bargains a realistic, problem-based method of color physics. The e-book makes a speciality of the main matters encountered in glossy color engineering, together with effective illustration of color info, Fourier research of reflectance spectra and complicated colorimetric computation. Emphasis is put on the sensible functions instead of the innovations themselves, with fabric established round key themes. those issues contain color calibration of visible monitors, desktop recipe prediction and versions for colour-appearance prediction.
each one subject is thoroughly brought at 3 degrees to help scholar figuring out. First, theoretical rules and historical past details are mentioned, then factors of mathematical recommendations persist with and eventually sensible recommendations are offered utilizing MATLAB. The content material comprises:
- A compendium of equations and numerical information required by way of the fashionable color and imaging scientist.
- Numerous examples of ideas and algorithms for a wide-range of computational difficulties in color technological know-how.
- Example scripts utilizing the MATLAB programming language.
This 2nd version includes vast new and revised fabric, together with 3 cutting edge chapters on color imaging, psychophysical tools, and physiological color areas; the MATLAB toolbox has been prolonged with a qualified, optimized, toolbox to head along the present educating toolbox; and a java toolbox has been extra as a way to curiosity clients who're writing net purposes and/or applets or cellular phone applications.
Computational color technological know-how utilizing MATLAB 2nd Edition is a useful source for college students taking classes in color technology, color chemistry and color physics in addition to technicians and researchers operating within the quarter. moreover, it acts an invaluable reference for pros and researchers operating in color based industries comparable to textiles, paints, print & digital imaging.
assessment from First Edition:
“…highly advised as a concise creation to the practicalities of color science…” (Color know-how, 2004)Content:
Chapter 1 creation (pages 1–11):
Chapter 2 Linear Algebra for newbies (pages 13–18):
Chapter three a quick advent to MATLAB® (pages 19–26):
Chapter four Computing CIE Tristimulus Values (pages 27–47):
Chapter five CIELAB and color distinction (pages 49–74):
Chapter 6 Chromatic?Adaptation Transforms and color visual appeal (pages 75–92):
Chapter 7 Physiological color areas (pages 93–118):
Chapter eight color administration (pages 119–129):
Chapter nine reveal Characterisation (pages 131–141):
Chapter 10 Characterisation of Cameras (pages 143–157):
Chapter eleven Characterisation of Printers (pages 159–178):
Chapter 12 Multispectral Imaging (pages 179–196):
Read or Download Computational Colour Science using MATLAB®, Second edition PDF
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Additional resources for Computational Colour Science using MATLAB®, Second edition
Example text
1(b). 05]; w = linspace(400,700,16); [P,S,mu] = polyfit(w,r,15); x = linspace(400,700,301); y = polyval(P,x,[],mu); Notice, however, that even using the normalised wavelength scale3 although the Lagrangian polynomial fits the 20-nm data exactly it would make quite poor estimates (particularly towards the two ends of the spectrum) if it was used to interpolate the 20-nm data to yield 10 nm intervals. The use of a Lagrangian polynomial in this way is not the best way to interpolate the spectral data; it may lead to wide excursions between the points and, moreover, a limited number of digits may lead to inaccuracies in the computed values (CIE, 2005).
This essentially involves fitting a fifth-order polynomial between two points Pi and Pi +1 but constraining the polynomial so that the gradients and curvature are consistent with the points Pi −2 , Pi −1 , and Pi +2 , Pi +3 . It turns out that the coefficients of the polynomial a0 − a5 are a linear transform of the points Pi −2 − Pi +3 and this transform is provided by the CIE (CIE, 2005), thus: a0 = Pi a1 = (2Pi −2 − 16Pi −1 + 16Pi +1 − 2Pi +2 )/24 3 The variable x is replaced by (x -mu(1))/mu(2).
13 can be replaced by the colour-matching functions and in this case the chromaticity coordinates are computed for the appropriate observer and for the equal-energy illuminant (illuminant E). However, in order to generate a smooth spectral locus then 5-nm intervals or less are required. It is sometimes desirable to generate a colour representation of the chromaticity diagram. Such a diagram would be useful for teaching and illustration purposes and also for technical papers and other publications.