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.


- 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

Show description

Read or Download Computational color technology PDF

Best imaging systems books

3D Videocommunication: Algorithms, concepts and real-time systems in human centred communication

The 1st booklet to provide a whole evaluate of 3D video expertise, 3D Videocommunication provides either theoretical research and implementation matters. The editors of this ebook are all key figures within the improvement of this new box and feature introduced jointly a few key specialists to supply a necessary assessment of 3D Video and the similar communications applied sciences at present below improvement.

Fiber Optics Handbook

Fiber optics is the most well liked subject in communications and this e-book from the world's top specialists truly lays out all of the info of optical communications engineering
* crucial technical advisor and recommendations equipment for the super-fast, super-broad fiber platforms and units powering the fastest-growing communications infrastructure
* equipment for producing above top performance
* transparent reasons and solutions to difficult demanding situations for WDM, DWDM, amplifiers, solitons, and different key applied sciences

Acoustical imaging : techniques and applications for engineers

''Acoustical Imaging begins with an creation to the fundamental theories and rules of acoustics and acoustical imaging, then progresses to debate its assorted functions: nondestructive checking out, scientific imaging, underwater imaging and SONAR and geophysical exploration. the writer attracts jointly different applied sciences, highlighting the similarities among subject components and their universal underlying conception.

Additional info for Computational color technology

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.

Download PDF sample

Rated 4.21 of 5 – based on 39 votes