Download e-book for kindle: Computational Colour Science using MATLAB®, Second edition by Stephen Westland, Caterina Ripamonti, Vien Cheung(auth.),

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 specializes in the major concerns encountered in sleek color engineering, together with effective illustration of color info, Fourier research of reflectance spectra and complex colorimetric computation. Emphasis is put on the sensible functions instead of the innovations themselves, with fabric dependent round key subject matters. those themes comprise color calibration of visible screens, desktop recipe prediction and types for colour-appearance prediction.

every one subject is punctiliously brought at 3 degrees to assist pupil realizing. First, theoretical rules and heritage details are mentioned, then factors of mathematical options persist with and eventually functional suggestions are awarded utilizing MATLAB. The content material comprises:

  • A compendium of equations and numerical info required through the trendy color and imaging scientist.
  • Numerous examples of options and algorithms for a wide-range of computational difficulties in color technology.
  • Example scripts utilizing the MATLAB programming language.

This 2nd version comprises tremendous new and revised fabric, together with 3 leading 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 with a view to curiosity clients who're writing net functions and/or applets or cellular phone applications.

Computational color technological know-how utilizing MATLAB 2nd Edition is a useful source for college kids taking classes in color technology, color chemistry and color physics in addition to technicians and researchers operating within the zone. moreover, it acts an invaluable reference for pros and researchers operating in color established industries corresponding to textiles, paints, print & digital imaging.

overview from First Edition:
“…highly suggested as a concise creation to the practicalities of color science…” (Color know-how, 2004)Content:
Chapter 1 advent (pages 1–11):
Chapter 2 Linear Algebra for newcomers (pages 13–18):
Chapter three a brief 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 show 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):

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Additional info for Computational Colour Science using MATLAB®, Second edition

Sample text

MATLAB’s patch command is useful for producing representations of various colour spaces. 4 Representation of an RGB colour-mixing triangle using the patch command. 5 The gamut of a digital display or printing system is smaller than the chromaticity gamut of all physically realisable colours. Therefore any chromaticity diagram that shows colours towards the spectral locus is, at best, an interesting representation rather than a colorimetrically accurate reproduction. 5 Representation of the CIE chromaticity diagram using the toolbox function, cieplot.

489999999999998. Notice that the fit is good but far from perfect. The problem is that matrix A is illconditioned. 0, a more accurate fit is possible using the same commands. 490000000000000 Note that the solution to the equation is correct to 15 decimal places. 63 × 102 . Alternatively, MATLAB provides the functions polyfit and polyval that automatically fit and use polynomials respectively. Thus the following code fits a fifteenthorder Lagrange polynomial to 16 reflectance values representing measurements at 20 nm The MATLAB command cond returns the so-called 2-norm condition number (the ratio of the largest singular value of the matrix to the smallest).

Note also the line: p = zeros(numSpectra,f*(N-5)+1); This pre-allocates sufficient memory for the matrix. This line is strictly not required. However, if memory is not pre-allocated in this way then MATLAB will need to call a memory allocation routine many times in the for loop; this method of allocating memory ‘on the fly’ is not recommended and can result in much slower code. 4 Computing CIE Tristimulus Values Extrapolation Methods A further problem that can occur when computing tristimulus values is that many reflectance spectrophotometers provide reflectance data in the range 400–700 nm and yet the 5 nm colour-matching functions, for example, are defined over 380–780 nm.

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