http://repository.ias.ac.in/39608/1/49_pub.pdf

A review of imperative technologies for wastewater treatment I: oxidation technologies at ambient conditions

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

A colour-difference equation based on CIELAB is developed. It includes not only lightness, chroma, and hue weighting functions, but also an interactive term between chroma and hue differences for improving the performance for blue colours and a scaling factor for CIELAB a* scale for improving the performance for gray colours. Four reliable colour discrimination datasets based upon object colours were accumulated and combined. The equation was tested together with the other advanced CIELAB based equations using the combined dataset and each individual dataset. It outperformed CMC and CIE94 by a large margin, and predicted better than BFD and LCD. The equation has been officially adopted as the new CIE colour-difference equation. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 340–350, 2001

The development of the CIE 2000 colour‐difference formula: CIEDE2000

Defining Color. Describing Color. Measuring Color. Measuring Color Quality. Colorants. Producing Colors. Back to Principles. Appendix. Bibliography. Index.

Billmeyer and Saltzman's Principles of Color Technology

This paper presents an automatic road-sign detection and recognition system based on support vector machines (SVMs). In automatic traffic-sign maintenance and in a visual driver-assistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, rectangular, triangular, and octagonal signs and, hence, covers all existing Spanish traffic-sign shapes. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The proposed recognition system is based on the generalization properties of SVMs. The system consists of three stages: 1) segmentation according to the color of the pixel; 2) traffic-sign detection by shape classification using linear SVMs; and 3) content recognition based on Gaussian-kernel SVMs. Because of the used segmentation stage by red, blue, yellow, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions

/pdf/road-sign-detection-and-recognition-based-on-support-vector-3indwhzhzq.pdf

Road-Sign Detection and Recognition Based on Support Vector Machines

Acknowledgements.1. Introduction.1.1 Who this book is for.1.2 Why base this book on MATLAB?1.3 A brief review of the CIE system of colorimetry.2. Linear Algebra for Beginners.2.1 Some basic definitions.2.2 Solving systems of simultaneous equations.2.3 Transposes and inverses.2.4 Linear and non-linear transforms.3. A Short Introduction to MATLAB.3.1 Matrix operations.3.2 Computing the transpose and inverse of matrices.3.3 M-files.3.4 Using functions in MATLAB.4. Computing CIE Tristimulus Values.4.1 Introduction.4.2 Standard colour-matching functions.4.3 Interpolation methods.4.4 Extrapolation methods.4.5 Tables of weights.4.6 Correction for spectral bandpass.4.7 Chromaticity diagrams.4.8 Implementation and examples.4.8.1 Spectral bandpass correction.4.8.2 Reflectance interpolation.4.8.3 Computing tristimulus values.4.8.4 Plotting the spectral locus.5. Computing Colour Difference.5.1 Introduction.5.2 CIELAB and CIELUV colour space.5.3 CIELAB colour difference.5.4 Optimized colour-difference formulae.5.4.1 CMC(l:c).5.4.2 CIE94.5.4.3 CIEDE2000.5.5 Implementations and examples.5.5.1 Computing CIELAB and CIELUV coordinates.5.5.2 Computing colour difference.6. Chromatic-adaptation Transforms and Colour Appearance.6.1 Introduction.6.2 CATs.6.2.1 CIECAT94.6.2.2 CMCCAT97.6.2.3 CMCCAT2000.6.3 CAMs.6.3.1 CIECAM97s.6.3.2 CMCCAM2000.6.4 Implementations and examples.6.4.1 CATs.6.4.2 Computing colour appearance.7. Characterization of Computer Displays.7.1 Introduction.7.2 Gamma.7.3 The GOG model.7.4 Device-independent transformation.7.5 Typical characterization procedure.7.6 Implementations and examples.8. Characterization of Cameras.8.1 Introduction.8.2 Correction for non-linearity.8.3 Device-independent representation.8.4 Implementations and examples.9. Characterization of Printers.9.1 Introduction.9.2 Physical models.9.3 Neural networks.9.4 Characterization of half-tone printers.9.4.1 Correction for non-linearity.9.4.2 Device-independent representation.9.4.3 The Kubelka-Munk model.9.5 Implementations and examples.9.5.1 Half-tone printer.9.5.2 Continuous-tone printer.10. Multispectral Imaging.10.1 Introduction.10.2 Computational colour constancy and linear models.10.3 Surface and illuminant estimation algorithms.10.4 Techniques for multispectral imaging.10.4.1 The Hardeberg method.10.4.2 The Imai and Berns method.10.4.3 Methods based on maximum smoothness.10.5 Implementations and examples.10.5.1 Deriving a set of basis functions.10.5.2 Representation of reflectance spectra in a linear model.10.5.3 Estimation of reflectance spectra from tristimulus values.10.5.4 Estimation of reflectance spectra from camera responses.10.5.5 Fourier operations on reflectance spectra.11. Colour Toolbox.11.1 cband.m (Box 1).11.2 pinterp.m (Box 2).11.3 r2xyz.m (Box 3).11.4 plocus.m (Box 4).11.5 xyz2lab.m (Box 5).11.6 lab2xyz.m (Box 6).11.7 xyz2luv.m (Box 7).11.8 car2pol.m (Box 8).11.9 pol2car (Box 9).11.10 cielabde.m (Box 10).11.11 dhpolarity (Box 11).11.12 cmcde.m (Box 12).11.13 cie94de.m (Box 13).11.14 cie00de.m (Box 14).11.15 cmccat97.m (Box 15).11.16 cmccat00.m (Box 16).11.17 ciecam97s.m (Box 17).11.18 gogtest.m (Box 18).11.19 compgog.m (Box 19).11.20 rgb2xyz.m (Box 20).11.21 xyz2rgb.m (Box 21).11.22 compigog (Box 22).11.23 getlincam.m (Box 23).11.24 lincam (Box 24).11.25 gettrc (Box 25).11.26 r2xyz (Box 26).References.Index.

Computational Colour Science using MATLAB

http://www.endoexperience.com/documents/Joiner3.pdf

A review of tooth colour and whiteness

The proliferation of low-cost colour imaging devices in the consumer market has led to a greater need to transfer images from one medium or device to another without loss of colour fidelity. A common solution is to characterise each device in terms of its CIE tristimulus values. In this paper two general techniques, artificial neural networks and polynomial transforms, are compared for their usefulness in characterising colour cameras. The neural and polynomial techniques are shown to give approximately similar performance once the parameters of the models are optimised. Since neural networks can be difficult and time-consuming to train, it is concluded that polynomial transforms offer the better alternative for camera characterisation.

/pdf/a-comparative-study-of-the-characterisation-of-colour-1jzjo8q5ai.pdf

A comparative study of the characterisation of colour cameras by means of neural networks and polynomial transforms

We investigate methods for the recovery of reflectance spectra from the responses of trichromatic camera systems and the application of these methods to the problem of camera characterization. The recovery of reflectance from colorimetric data is an ill-posed problem, and a unique solution requires additional constraints. We introduce a novel method for reflectance recovery that finds the smoothest spectrum consistent with both the colorimetric data and a linear model of reflectance. Four multispectral methods were tested using data from a real trichromatic camera system. The new method gave the lowest maximum colorimetric error in terms of camera characterization with test data that were independent of the training data. However, the average colorimetric performances of the four multispectral methods were statistically indistinguishable from each other but were significantly worse than conventional methods for camera characterization such as polynomial transforms.

Characterization of trichromatic color cameras by using a new multispectral imaging technique

In this study, two All-Ceramic (AC) materials--Empress 2 (EMP) (Ivoclar Vivadent AG, Schaan, Liechtenstein) and In-Ceram ALUMINA (ICA) (Vita Zahnfabrik, Bad Sackingen, Germany)--were analyzed, along with the effects of 3 luting agents-viz. Zinc Phosphate cement (ZNPO, PhospaCEM PL, Ivoclar Vivadent AG, Schaan, Liechtenstein), Glass Ionomer Cement (GIC, Ketac-Cem Radiopaque, ESPE Dental AG, Seefeld, Germany), and Compolute (COMP, ESPE Dental AG, Seefeld, Germany)--on the final color, using the CIELab system. Color differences (DeltaL, Deltaa, Deltab, and DeltaE) were calculated for samples with luting agents and for samples without luting agents with standard white and black backgrounds, with the use of a spectrophotometer, Luci 100 (Dr. Lange, Berlin, Germany). One-way ANOVA for DeltaL, Deltaa, Deltab, and DeltaE within both the AC systems, with and without luting agents, showed significant contributions of the background (p < 0.05). EMP was seen to be more translucent than ICA. Darker ceramics showed less color variation. Luting agents altered the final color of the restoration. ZNPO was least translucent, followed by GIC and COMP. Marginal increases in thicknesses of ICA samples (0.4 mm) do not show a statistically significant color difference. No method exists to predict the outcome of an AC restoration based on consideration of the luting agent and the background color.

Stephen Westland

Papers

Computational Colour Science using MATLAB

A review of tooth colour and whiteness

A comparative study of the characterisation of colour cameras by means of neural networks and polynomial transforms

Characterization of trichromatic color cameras by using a new multispectral imaging technique

Spectrophotometric Analysis of All-ceramic Materials and Their Interaction with Luting Agents and Different Backgrounds