A low‐cost four‐colour (RBYK) dye‐based ink‐jet printing system for textiles was introduced in this study, in which red and blue inks were employed instead of the magenta and cyan inks used in half‐tone printing. The basis of a colour‐management system for this device was developed by determining the mapping between XYZ tristimulus values of output colours and the digital RBYK values using polynomial transforms. A second‐order equation was found to give the best performance with an average characterisation error of under 7 CIELAB units.
Sixteen students were involved in an experiment to describe the appearance of colour samples. These students were divided into two groups: design background and chemistry/engineering background.Design participants react to samples with images and adjectives describing their feelings and emotions.They tend to use evocative, emotional, and associative terms which are also related to use of a wide variety of semantic fields. On the other hand, chemistry/engineering participants focused heavily on colour and surface objectively. Although they used limited sematic fields compared to design participants, they used more precise language in their description. In terms of describing the process of changing the appearance of one sample to another, participants from chemistry/engineering used technical terms and described the process more systematically in comparison to the design participant group. The main focus of this research was to investigate differences in colour communication between participants from the disciplinary backgrounds. These comparisons are not intended to suggest negative or positive judgements by the researchers but to describe the different values of these participants. This research provides evidence that people from different disciplines who need to collaborate in colour design use different colour vocabularies.
Spectral images contain a large volume of data and can be efficiently compressed by low dimensional linear models. However, there is a trade-off between the accuracy of spectral and colorimetric representation. When a spectral image is reproduced by a low-dimensional linear model, spectral error and color difference are contrary to each other and minimizing the colour error is by no means equivalent to minimizing the spectral error. Although one aim of a spectral-image file format is to preserve and represent the spectral information, most users are likely to reproduce a spectral image on a trichromatic image-reproduction system and therefore it is important that the spectral information is not preserved at the expense of colorimetric accuracy. In this study a method for spectral encoding that provides an efficient representation of the spectral information whilst perfectly preserving the colorimetric information is analysed. The lossy compression technique that is considered in this work is based on a low-dimensional linear model of spectral reflectance, with the first three basis functions represent color information and the additional basis functions are metameric blacks which preserve spectral information.
Short-wavelength light has been known to have an alerting effect on human alertness in the night-time.However, there is very few study focus on the effect of intensity of light on alertness.In this study we evaluated alerting ability of short-wavelength light of three different intensities (40lx, 80lx and 160lx).Eight subjects participated in a 60-minute exposure protocol for four evenings, during which electroencephalogram (EEG) as well as subjective sleepiness was collected.EEG power in the beta range was significantly higher after subjects were exposed to 160lx light than after they were exposed to 40lx, 80lx light or remained in darkness.Also, the alpha theta was significantly lower under 160lx light then in darkness.These results showed that the effect of intensity on alertness is not linear and further work should be done to investigate the threshold intensity that is required to produce alerting effect.
A number of different methods exist for the color characterization of imaging devices such as digital camera systems. In this study, the use of high-order polynomials and artificial neural networks for color camera characterization are compared and contrasted. A quantitative evaluation of their performance is determined for a typical commercial camera system. The importance of independent training and testing sets is stressed and the effect of the number of samples in the training set is evaluated. The results show that, if the best performance is considered, the two models are approximately comparable. Any performance advantage obtained from using a neural network for device characterization does not seem to be warranted given the additional risks of using such systems. The effect of training set size seems surprisingly small for both polynomial and neural systems with generalization performance only being seriously affected for training set sizes less than about 100.
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