Download Computational color technology by Henry R. Kang PDF

By Henry R. Kang

Henry Kang presents the basic colour ideas and mathematical instruments to arrange the reader for a brand new period of colour copy, and for next purposes in multispectral imaging, scientific imaging, distant sensing, and computer imaginative and prescient. This booklet is meant to bridge the distance among colour technological know-how and computational colour expertise, placing colour edition, colour fidelity, colour transforms, colour reveal, and colour rendition within the area of vector-matrix representations and theories. Computational colour Technology bargains with colour electronic pictures at the spectral point utilizing vector-matrix representations in order that the reader can learn how to procedure electronic colour photos through linear algebra and matrix theory.


- Preface

- Acknowledgments

- Tristimulus Specification

- colour rules and houses

- Metamerism

- Chromatic variation

- CIE colour areas

- RGB colour areas

- Device-Dependent colour areas

- Regression

- three-d search for desk with Interpolation

- Metameric Decomposition and Reconstruction

- Spectrum Decomposition and Reconstruction

- Computational colour fidelity

- White-Point Conversion

- Multispectral Imaging

- Densitometry

- Kubelka-Munk concept

- Light-Reflection version

- Halftone Printing versions

- problems with electronic colour Imaging

- Appendix 1: Conversion Matrices

- Appendix 2: Conversion Matrices from RGB to ITU-R.BT.709/RGB

- Appendix three: Conversion Matrices from RGB to ROMM/RGB

- Appendix four: RGB Color-Encoding criteria

- Appendix five: Matrix Inversion

- Appendix 6: colour mistakes of Reconstructed CRI Spectra with admire to Measured Values

- Appendix 7: colour mistakes of Reconstructed CRI Spectra with recognize to Measured Values utilizing Tristimulus Inputs

- Appendix eight: White-Point Conversion Accuracies utilizing Polynomial Regression

- Appendix nine: electronic Implementation of the protecting Equation

- Index

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0 en en x¯n en y¯n . 7 Stimulus Function For the associative relationship of (η = ES) in Eq. 3b), we obtain a vector η , a color stimulus function or color signal received by the eyes. It is the product of the object and illuminant spectra, having n elements.  e1  0   η = ES =  0 ... ... 0 0 0 e2 0 0 e3 ... ... 0 0 ... 0 ... ... ... ... ...     s1 e1 s1 ... 0 . . 0   s2   e2 s2      . . 0   s3  =  e3 s3  .     ...   ...   ...   ... 8 Perceived Object The expression (QT = A T S) in Eq.

Substituting Eq. 29) into Eq. 30), we obtain Aj = V Φ TV V TV −1 V T + ΦjT I − V V T V −1 VT V −1 . 31) The second term of Eq. org/terms Metamerism 37 be used, for that matter. By using the same identity relationship, the first term becomes Φ T V . Aj = V Φ T V −1 . 32) Comparing Eq. 32) to Eq. 7) A = V (Φ T V )−1 , it is clear that Aj = A. This proof implies that all primary sets generate equivalent colormatching functions and it is only the projection of the primaries onto the visual space that matters.

6,7 The sampled visual spectral sensitivity is represented by a matrix of three vectors V = [V 1 , V 2 , V 3 ], one for each cone type, where V i is a vector of n elements. Compared to the CMF curves of Fig. 2, they differ in several ways: first, the visual spectral sensitivity has no negative elements; second, the overlapping of green (middle-wavelength) and red (long-wavelength) components is much stronger. The sensor responses to the object spectrum η(λ) can be represented by Υ = V T η. 1 Relative spectral absorptances of human cone pigments measured by microspectrophotometry.

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