Computational color technology by Henry R. Kang

By Henry R. Kang

Henry Kang offers the elemental colour ideas and mathematical instruments to organize the reader for a brand new period of colour copy, and for next functions in multispectral imaging, scientific imaging, distant sensing, and laptop imaginative and prescient. This publication is meant to bridge the space among colour technological know-how and computational colour know-how, 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 photos at the spectral point utilizing vector-matrix representations in order that the reader can learn how to strategy electronic colour pictures through linear algebra and matrix theory.

Contents

- Preface

- Acknowledgments

- Tristimulus Specification

- colour ideas and homes

- Metamerism

- Chromatic model

- CIE colour areas

- RGB colour areas

- Device-Dependent colour areas

- Regression

- 3-dimensional 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 types

- 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 appreciate to Measured Values

- Appendix 7: colour mistakes of Reconstructed CRI Spectra with admire 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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Example text

6) carries from i = 0 to i = n. The inverse of the matrix M a is called M e . M e = M −1 a . 1 gives the values of M a and M e for both CIE 1931 and 1964 standard observers using the color-matching functions with the range from 390 to 710 nm at 10-nm intervals. For a set of mutual metamers, η1 (λ), η2 (λ), η3 (λ), . . , ηm (λ) that are sampled in the same rate as the color-mixing function A, we have a set of vectors, η1 , η2 , η3 , . . , ηm , where each ηi is a vector of n elements. Using the vector-space representation, we can represent Eq.

4 Bartleson Transform The Bartleson transform is based on the von Kries hypothesis with nonlinear adaptations.

22) This proves that R is invariant under transformation of primaries. Hence, η´ is invariant. 1 for comparisons). 1 gives the 3D plot of matrix R. There are three peaks in the diagram. 19 (7) If η is a unit monochromatic radiation of wavelength λi , then Rη or η´ becomes a simple copy of the ith column of R. This indicates that each column or row of the matrix R (R is symmetric) is the fundamental metamer of a unit monochromatic stimulus. Therefore, η´ is a weighted sum of the fundamental metamers of all the monochromatic stimuli that constitute η.

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