By Henry R. Kang
Henry Kang offers the elemental colour rules and mathematical instruments to organize the reader for a brand new period of colour replica, and for next purposes in multispectral imaging, clinical imaging, distant sensing, and laptop imaginative and prescient. This booklet is meant to bridge the distance among colour technology and computational colour know-how, placing colour variation, colour fidelity, colour transforms, colour reveal, and colour rendition within the area of vector-matrix representations and theories. Computational colour Technology offers with colour electronic pictures at the spectral point utilizing vector-matrix representations in order that the reader can discover ways to procedure electronic colour photographs through linear algebra and matrix theory.
- Tristimulus Specification
- colour ideas and houses
- Chromatic variation
- CIE colour areas
- RGB colour areas
- Device-Dependent colour areas
- three-d look up desk with Interpolation
- Metameric Decomposition and Reconstruction
- Spectrum Decomposition and Reconstruction
- Computational colour fidelity
- White-Point Conversion
- Multispectral Imaging
- Kubelka-Munk conception
- 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 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
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Extra resources for Computational color technology
Z¯ n e3 x¯3 e3 y¯3 e3 z¯ 3 ... ... 0 0 e2 0 0 e3 ... ... 0 ... ... 0 ... ... ... 0 ... 0 ... 0 ... ... 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 ...
4 Transitivity Law Because the color-matching matrix A is a linear transform of the human visual sensitivity, we can represent the spectral sensitivity of the eye by the matrix A. 5 AT η = Υ . 16) This transform reduces the dimension from n to 3, and causes a large loss in information, suggesting that many different spectra may give the same color appearance to the observer. In this case, two stimuli ηA (λ) and ηB (λ) are said to be a metameric match if they appear the same to the human observer.
The set of observer stimulation levels that can be produced using nonnegative light-source levels forms a convex subset of the space of all possible stimulation levels. 1. The subset of stimulation levels possible with arbitrary nonnegative spectral distribution is, itself, convex and bounded by the stimulation levels produced by monochromatic light sources. Constraint 5. The problem of the determination of suitable image generator control inputs when there are more than three light sources can be posed as a problem in linear programming.
Computational color technology by Henry R. Kang