Estimating the Colors of Paintings

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• 作者：S茅rgio M. C. Nascimento (16) r> Jo茫o M. M. Linhares (16) r> Catarina A. R. Jo茫o (16) r> Kinjiro Amano (17) r> Cristina Montagner (18) r> Maria J. Melo (18) r> Marcia Vilarigues (18) r>
• 关键词：Colors of paintings ; Color vision ; Art visualization ; Color rendering ; Aesthetics
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9016
• 期：1
• 页码：236-242
• 全文大小：465 KB
• 参考文献：1. Pinto, P.D., Linhares, J.M., Carvalhal, J.A., Nascimento, S.M.: Psychophysical estimation of the best illumination for appreciation of Renaissance paintings. Vis Neurosci <strong class="a-plus-plus">23strong>, 669鈥?74 (2006) ss="external" href="http://dx.doi.org/10.1017/S0952523806233340" target="_blank" title="It opens in new window">CrossRef r> 2. Pinto, P.D., Linhares, J.M.M., Nascimento, S.M.C.: Correlated color temperature preferred by observers for illumination of artistic paintings. Journal of the Optical Society of America A-Optics Image Science and Vision <strong class="a-plus-plus">25strong>, 623鈥?30 (2008) ss="external" href="http://dx.doi.org/10.1364/JOSAA.25.000623" target="_blank" title="It opens in new window">CrossRef r> 3. Nascimento, S.M.C., Masuda, O.: Best lighting for visual appreciation of artistic paintings-experiments with real paintings and real illumination. Journal of the Optical Society of America A-Optics Image Science and Vision <strong class="a-plus-plus">31strong>, A214鈥揂219 (2014) ss="external" href="http://dx.doi.org/10.1364/JOSAA.31.00A214" target="_blank" title="It opens in new window">CrossRef r> 4. Liu, A., Tuzikas, A., Zukauskas, A., Vaicekauskas, R., Vitta, P., Shur, M.: Cultural Preferences to Color Quality of Illumination of Different Artwork Objects Revealed by a Color Rendition Engine. Photonics Journal, IEEE <strong class="a-plus-plus">5strong>, 6801010 (2013) ss="external" href="http://dx.doi.org/10.1109/JPHOT.2013.2276742" target="_blank" title="It opens in new window">CrossRef r> 5. Regan, B.C., Reffin, J.P., Mollon, J.D.: Luminance Noise and the Rapid-Determination of Discrimination Ellipses in Color Deficiency. Vision Research <strong class="a-plus-plus">34strong>, 1279鈥?299 (1994) ss="external" href="http://dx.doi.org/10.1016/0042-6989(94)90203-8" target="_blank" title="It opens in new window">CrossRef r> 6. Rodriguez-Carmona, M.: Variability of Chromatic Sensitivity: fundamental studies and clinical applications. vol. PhD. City University London (2006) r> 7. Graham, D.J., Redies, C.: Statistical regularities in art: Relations with visual coding and perception. Vision Research <strong class="a-plus-plus">50strong>, 1503鈥?509 (2010) ss="external" href="http://dx.doi.org/10.1016/j.visres.2010.05.002" target="_blank" title="It opens in new window">CrossRef r> 8. Graham, D.J., Field, D.J.: Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities. Spatial Vision <strong class="a-plus-plus">21strong>, 149鈥?64 (2008) ss="external" href="http://dx.doi.org/10.1163/156856808782713771" target="_blank" title="It opens in new window">CrossRef r>
• 作者单位：S茅rgio M. C. Nascimento (16) r> Jo茫o M. M. Linhares (16) r> Catarina A. R. Jo茫o (16) r> Kinjiro Amano (17) r> Cristina Montagner (18) r> Maria J. Melo (18) r> Marcia Vilarigues (18) r>r>16. Centre of Physics, Campus de Gualtar, University of Minho, 4710-057, Braga, Portugal r> 17. School of Electrical and Electronic Engineering, University of Manchester, Manchester, M13 9PL, UK r> 18. Department of Conservation and Restauration, Faculdade de Ci锚ncias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal r>
• 丛书名：Computational Color Imaging
• ISBN：978-3-319-15979-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Roboticsr>Computer Communication Networksr>Software Engineeringr>Data Encryptionr>Database Managementr>Computation by Abstract Devicesr>Algorithm Analysis and Problem Complexityr>
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Observers can adjust the spectrum of illumination on paintings for optimal viewing experience. But can they adjust the colors of paintings for the best visual impression? In an experiment carried out on a calibrated color monitor images of four abstract paintings obtained from hyperspectral data were shown to observers that were unfamiliar with the paintings. The color volume of the images could be manipulated by rotating the volume around the axis through the average (a*, b*) point for each painting in CIELAB color space. The task of the observers was to adjust the angle of rotation to produce the best subjective impression from the paintings. It was found that the distribution of angles selected for data pooled across paintings and observers could be described by a Gaussian function centered at 10o, i.e. very close to the original colors of the paintings. This result suggest that painters are able to predict well what compositions of colors observers prefer.