By Henry R. Kang
Henry Kang offers the basic colour ideas and mathematical instruments to organize the reader for a brand new period of colour replica, and for next functions in multispectral imaging, clinical imaging, distant sensing, and computer imaginative and prescient. This publication is meant to bridge the space among colour technological know-how 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 bargains with colour electronic pictures at the spectral point utilizing vector-matrix representations in order that the reader can discover ways to procedure electronic colour photos through linear algebra and matrix theory.
- Tristimulus Specification
- colour ideas and homes
- 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 blunders of Reconstructed CRI Spectra with recognize 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 overlaying Equation
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Extra resources for Computational color technology
Kulkarni, Sampling and processing of color signals, IEEE Trans. Image Proc. 5, pp. 677–681 (1996). 6. H. J. Trussell, A review of sampling effects in the processing of color signals, IS&T and SID’s 2nd Color Imag. , pp. 26–29 (1994). 7. F. W. Billmeyer and M. Saltzman, Principles of Color Technology, 2nd Edition, Wiley, New York, Chap. 2 (1981). 8. CIE, Colorimetry, Publication No. 15, Bureau Central de la CIE, Paris (1971). 9. G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition, Wiley, New York, p.
It has many industry applications, such as the cross-rendering between different media. There are two main approaches; one is the chromatic adaptation employing visual evaluations, and the other one is a mathematic computation of the color constancy (or white conversion). The main goal of the computational color constancy is to predict the object’s surface reflectance. If the surface spectrum is known, the correct tristimulus can be computed under any adapted illumination. The computational color constancy will be presented in Chapter 12.
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 ... 0 . . 0 s2 e2 s2 . . 0 s3 = e3 s3 . ... ... ... ...