2008
DOI: 10.1016/j.eswa.2007.02.036
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Modelling of colour perception of different age groups using artificial neural networks

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Cited by 38 publications
(12 citation statements)
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“…It is found that the results of most of the color studies have been done in laboratory or testing room, or using color samples and simulated images in articles, or computer programs with color samples or other color elements . Nevertheless, few studies have combined the method assessment with an actual setting context in order to evaluate the effects of participants in this context .…”
Section: Resultsmentioning
confidence: 99%
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“…It is found that the results of most of the color studies have been done in laboratory or testing room, or using color samples and simulated images in articles, or computer programs with color samples or other color elements . Nevertheless, few studies have combined the method assessment with an actual setting context in order to evaluate the effects of participants in this context .…”
Section: Resultsmentioning
confidence: 99%
“…The research presented here is based on a comprehensive literature study incorporating various research articles from different disciplines, such as optics, 37,38 image processing and neurocomputing, 39,40 human and social environment, 41 psychology, 42,43 engineering, [44][45][46] geriatric medicine and health science, 21,34,47 environment and information science, 48,49 imaging and printing technology, 50 and electronics. 51 Regarding to the participants of the studies analyzed, in general, they mix young and aged participants in order to compare the results obtained in lifespan. For young participants, it is reported mean ages ranging from 6 to 30 years.…”
Section: Discussionmentioning
confidence: 99%
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“…For instance, colour can add reality, assist the viewer to discriminate between visual elements, focus attention on the important information (e.g. by using salience), code and link logically related elements, create impressions, and generate emotional responses (Jones, 1997;Kose, 2008;Pett & Wilson, 1996). Background.…”
Section: Laying the Foundationmentioning
confidence: 99%
“…The basic advantage of ANN is that it does not need any mathematical model; an ANN learns from examples and recognizes patterns in a series of input and output data without any prior assumptions about their nature and interrelations [17]. Moreover, ANN is a good alternative to conventional empirical modeling based on polynomial and linear regressions [18]. Thus, ANN is a typical non-mechanistic model for modeling complex information and is known to have two intrinsic advantages.…”
Section: Introductionmentioning
confidence: 99%