Color-Accurate Image Archives Using Spectral Imaging

Roy S. Berns

Munsell Color Science Laboratory

Chester F. Carlson Center for Imaging Science

Rochester Institute of Technology

Rochester, New York

ABSTRACT

Digital imaging that includes spectral estimation can overcome limitations of typical digital photography, such as limited color accuracy and constraints to a predefined viewing condition or a specific output device. An example includes the use of ICC color management to generate an archive of images rendered for a specific display or for a specific printing technology. A spectral image offers enhanced opportunities for image analysis, art conservation science, lighting design, and an archive that can be used to relate back to an object’s physical properties. The Munsell Color Science Laboratory at Rochester Institute of Technology is involved in a joint research program with the National Gallery of Art in Washington, D.C., and the Museum of Modern Art in New York to develop a spectral-imaging system optimized for artwork imaging, archiving, and reproduction. Progress is being documented at the website www.art-si.org. This paper summarizes the scientific approach.

INTRODUCTION

Imaging is an important technique in the scientific examination of art. Its main use has been for visual documentation. Photographs have long been used to document condition before and after transit, microscopic examinations, conservation treatments, and so on. They are used to enable color reproductions in books and from the Internet. Images using materials with spectral sensitivities in



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Scientific Examination of Art: Modern Techniques in Conservation and Analysis Color-Accurate Image Archives Using Spectral Imaging Roy S. Berns Munsell Color Science Laboratory Chester F. Carlson Center for Imaging Science Rochester Institute of Technology Rochester, New York ABSTRACT Digital imaging that includes spectral estimation can overcome limitations of typical digital photography, such as limited color accuracy and constraints to a predefined viewing condition or a specific output device. An example includes the use of ICC color management to generate an archive of images rendered for a specific display or for a specific printing technology. A spectral image offers enhanced opportunities for image analysis, art conservation science, lighting design, and an archive that can be used to relate back to an object’s physical properties. The Munsell Color Science Laboratory at Rochester Institute of Technology is involved in a joint research program with the National Gallery of Art in Washington, D.C., and the Museum of Modern Art in New York to develop a spectral-imaging system optimized for artwork imaging, archiving, and reproduction. Progress is being documented at the website www.art-si.org. This paper summarizes the scientific approach. INTRODUCTION Imaging is an important technique in the scientific examination of art. Its main use has been for visual documentation. Photographs have long been used to document condition before and after transit, microscopic examinations, conservation treatments, and so on. They are used to enable color reproductions in books and from the Internet. Images using materials with spectral sensitivities in

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis such non-visible regions of the electromagnetic spectrum as infrared and X ray are equally important to the visible spectrum. Although images are used to record scientific examinations, they are used infrequently as an analytical tool, that is, the amount of colorant in a photographic material would be used to relate to physical properties of the art. In contrast, astronomy, remote sensing, and medicine have exploited this capability for many years. The advent of digital imaging offers increased opportunities to exploit images for the scientific examination of art. A research program is underway at Rochester Institute of Technology to develop an image-acquisition system that records reflection information as a function of wavelength. The system initially is limited to the visible region. This publication will summarize our methodologies and give some performance examples. Full results, documentation, and demonstrations can be downloaded and viewed at www.art-si.org. At the end of this paper are relevant publications written by students, faculty, and staff of the Munsell Color Science Laboratory. TECHNICAL APPROACH Complete Sampling—Spectral Measurement A spectrophotometer records spectral reflectance or transmittance for a specific circular aperture; a single color is measured. By analogy a spectral-imaging system records spectral reflectance or transmittance for a projected scene at a specific spatial resolution; many colors are measured. One can envision a number of techniques to disperse light onto a detector plane. The technique we have taken is to couple a monochrome, area-array chaged-couple device detector with a liquid-crystal tunable filter. Successive images are captured, each image centered at a specific wavelength. Typically we capture 31 bands corresponding to 400-700 nm at 10 nm increments. As a measurement device, calibration is necessary. For each band, images are taken of a dark field (to remove fixed-pattern noise), several neutral diffuse papers (to compensate for lighting non-uniformity and optical flare), a pressed polytetrafluoroethylene tablet (to determine optimal exposure time), and a color target made from a number of colorants (to compensate for wavelength and geometry bandwidth). These targets are crucial to achieve acceptable performance. In general, spectroradiometry and imaging have greater uncertainty than contact spectrophotometry. Thus, it is necessary to derive transformations that minimize these uncertainties. A typical transformation is shown in Figure 1. The GretagMacbeth ColorChecker DC and a custom target of blue pigments mixed with titanium white were used to develop the transformation. This figure is a visualization of the matrix transformation from spatially corrected 31-band images to spectral reflectance factor images. The matrix contains 961 coefficients

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis FIGURE 1 Visualization of calibration matrix for 31-band image-acquisition system. (31 × 31). Ideally the matrix should have dominant diagonal and small off-diagonal coefficients. Figure 2 is an image of the well-known color target, the GretagMacbeth ColorChecker Color Rendition Chart. This independent evaluation target provides a method to benchmark color and spectral accuracy. Typical performance is shown in Figure 3 for these colors. The spectral accuracy was 1.4 percent root-mean-square (RMS) reflectance and an average color accuracy of 1.5ΔE00 under daylight (D65) and viewed by the 1931 CIE standard observer. Subsampling—Spectral Estimation The system described in the previous section performs spectral measurement; there are the same numbers of image bands as wavelengths. The majority of natural and synthesized colorants have large-bandwidth absorption spectra in the visible region. Furthermore, there are not many sharp transitions from high to low reflectance (and vice versa). From a dimensionality reduction perspective it may not be necessary to collect images every 10 nm, that is, sub-sampling may not result in a loss of accuracy. For example, during the 1970s, many spectrophotom-

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis FIGURE 2 GretagMacbeth ColorChecker Color Rendition Chart. FIGURE 3 Typical spectral-measurement accuracy for the ColorChecker using a 31-band image-acquisition system (blue lines) compared with a small-aperture contact spectrophotometer (red lines).

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis eters used in color technology sampled the visible spectrum in 20-nm-wavelength increments and bandwidth. As the number of direct measurements reduces we are performing spectral estimation rather than spectral measurement. Suppose a painting was created from a single chromatic colorant and white (or paper in the case of a watercolor). Because the concentration of one colorant is being varied, one can measure the light reflection at a single wavelength, usually the wavelength of maximum absorption (minimum reflectance ignoring the white). A single image is captured; differences in gray level relate to differences in colorant concentration. At this wavelength, changes in concentration will result in the greatest change in reflectance (i.e., the greatest image contrast). If we measure the spectral absorption properties of the colorant using a spectrophotometer and determine the relationship between camera signals and concentration and between concentration and spectral reflectance (e.g., Kubelka-Munk theory, Beer’s law), the single image can be used to estimate a 31-band spectral image. This estimation process has also enabled significant data reduction. We need to archive only the single-band image. The spectral reflectances of the colorant and white, the transformation from camera signals to concentration, and between concentration and spectral reflectance are stored in the image tag. This is analogous to an ICC input profile except in this case, the profile performs spectral color management. This idea is extended to paintings created with many colorants. Principal component analysis (PCA) is used to define a set of statistical colorants. Because of the spectral properties of colorants in the visible region, the number of statistical colorants (eigenvectors) can vary between 5 and 16. The specific number depends on spectral accuracy requirements and the samples analyzed statistically. In general the imaging system captures the same number of images as the number of statistical colorants. A relationship is determined between the camera signals and statistical colorant amounts (principal components). Concatenating these various steps results in a transformation that relates camera signals to spectral reflectance. Principal component analysis can be interpreted as constraining the spectral outcome of the mathematical transformation, a type of spectral interpolation. With a large enough number of samples, we can eliminate the use of PCA. In its place we derive a direct transformation from camera signals to spectral reflectance. This method uses a singular-value-decomposition-based pseudo-inverse calculation in which several hundred thousand samples are used to estimate several-hundred-transformation coefficients. These many samples are acquired by considering each pixel of an image an individual data point. We have found that these two methods yield similar spectral accuracy. Both techniques are constrained in two ways. The first has to do with the camera. Performance depends on the spectral sensitivities of each camera channel. Optimal filter design has been studied for many years; unfortunately these filters, designed by simulation, cannot be fabricated. The practical solution is to

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis select the best filters from those produced commercially. We have taken this approach. We have also used commercial cameras with color filter area-array sensors. With additional filtration using colored absorption filters, sets of color images are recorded. Three, six, or nine image planes (each triplet is the usual red, green, blue image) are related to three, six, or nine statistical colorants or directly to spectral reflectance. The second constraint is the dependence on a color target. The target is used to derive the mathematical transformation. Ideally, the target should have a number of colored patches sampling thoroughly the color gamut of materials to be imaged. The patches should be made from colorants with unique spectral properties. The gloss properties should be consistent. In essence there is an assumption that the color target has spectral properties that encompass those of the art to be imaged. Most commercial targets do not have these ideal properties. Despite these constraints, the method has proven to be nearly equivalent to 31-band spectral imaging. Using a color-filter-array camera and two absorption filters, the average performance for the ColorChecker was 1.6 percent RMS and 1.2 ΔE00, plotted in Figure 4. The transformation matrix is plotted in Figure 5, derived using the ColorChecker DC. This transformation relates six camera sig- FIGURE 4 Typical spectral-measurement accuracy for the ColorChecker using a two-filter color-filter-array image-acquisition system (blue lines) compared with a small-aperture contact spectrophotometer (red lines).

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis FIGURE 5 Visualization of calibration matrix for six-band image-acquisition system, achieved using two absorption colored glass filters and a color-filter area-array image-acquisition system. nals to 36 wavelengths, totaling 216 matrix coefficients. At each wavelength, there should be at least one peak or valley. Spectral Advantage A spectral image archive has a number of advantages over many current image archives. Sometimes, an archive is created by digitizing photographs. In other cases direct digitization is used with scanbacks, using repurposed flatbed scanning sensors. Film and scanbacks have spectral sensitivities quite different from the human visual system. As a result these archives require significant visual editing as part of the workflow. Thus, the archive is connected to a particular display, viewing condition, and observer. Color accuracy is limited. Color management principles can be used to reduce the reliance on visual editing. Even so, color accuracy can still be limited. The spectral archive is not subject to these constraints; the result is excellent color accuracy, eliminating the need for visual editing. A non-spectral archive stores three image planes per object, such as RGB

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis TIFF (tagged image file format). For color-managed images tags are used to relate the digital signals to standardized viewing and illuminating conditions (source profiles). Using ICC color management, this includes CIE illuminant D50 and the CIE 1931 standard observer. Thus, the archive is limited to a single observer and illuminant. The spectral archive can be used to relate the digital signals to any observer, viewing, and illuminating condition. This provides tremendous opportunities by enabling an object to be rendered under multiple conditions without re-imaging. Using vision models that account for chromatic adaptation, one can compare an object’s appearance with changes in lighting, providing lighting designers with a unique and powerful tool. Many colorants have unique spectral properties within the visible spectrum. Thus, the spectral archive can be used to analyze the colorants used in a work of art. The spectral information can aid conservators in selecting colorants for inpainting (retouching) that result in minimal metamerism. We expect that a combination of spectral imaging and direct small-aperture spectrophotometry can be used to create colorant maps. Printed reproductions are quite useful for scholarly endeavors and during conservation treatments. Color-managed prints are designed to match under CIE illuminant D50 and to be viewed by the 1931 standard observer. By definition the prints are metameric and will only match for this single condition. However, prints are viewed under a variety of conditions. Spectral data can be used to produce prints that better match original objects for these many conditions. Finally, a visible-spectrum archive can be combined with other wavelength regions such as infrared and X ray, aiding in a more complete record on a work of art’s physical properties. CONCLUSIONS A spectral image archive results in high color accuracy and facilitates the scientific examination of art in the visible region of the electromagnetic spectrum. Two methods of image acquisition have been described: (1) complete spectral sampling and (2) spectral sub-sampling combined with estimation. Each method has advantages and disadvantages. Issues include spectral accuracy, colorimetric accuracy, hardware complexity and cost, software complexity, image capture time, data storage, ease of use, maintenance, and system duplication complexity. One of the research goals is to describe these trade-offs in order to provide museums, archives, and libraries with information to assist them in making practical decisions regarding the incorporation of spectral imaging into their imaging practices.

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis ACKNOWLEDGEMENTS This research is supported by the Andrew W. Mellon Foundation; Rochester Institute of Technology; the National Gallery of Art, Washington, D.C.; and the Museum of Modern Art, New York, and would not have been possible without the participation of the students, faculty, and staff of the Munsell Color Science Laboratory. RELEVANT MUNSELL COLOR SCIENCE LABORATORY (MCSL) PUBLICATIONS Publications 1994 Vent. D. S. Multichannel analysis of object-color spectra. M.S. Thesis, Rochester Institute of Technology, Rochester, N.Y. 1996 Burns, P. D. and R. S. Berns. Analysis of multispectral image capture. In Proceedings of the IS&T/SID Fourth Color Imaging Conference Color Science, Systems, and Applications, pp. 19-22. Springfield, Va.: Society for Imaging Science and Technology. 1997 Burns, P. D. Analysis of image noise in multi-spectral color acquisition. Ph.D. Dissertation, Rochester Institute of Technology, Rochester, N.Y. Burns, P. D. and R. S. Berns. Error propagation in color signal transformations. Color Research and Application 22:280-289. Burns, P. D., and R. S. Berns. Modeling colorimetric error in electronic image acquisition, Proceedings of the Optical Society of America Annual Meeting, pp. 147-149. Washington, DC: Optical Society of America. 1998 Berns, R. S., F. H. Imai, P. D. Burns, and D. Tzeng. Multispectral-based color reproduction research at the Munsell Color Science Laboratory. In Proceedings of the International Society for Optical Engineering, vol. 3409, ed. J. Bares, pp. 14-25. Bellingham, Wash.: International Society for Optical Engineering. Imai, F. H. and R. S. Berns. High-resolution multi-spectral image capture for fine arts preservation. In Proceedings of the Fourth Argentina Color Conference, pp. 21-22. Buenos Aires, Argentina: Grupo Argentino del Color.

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis Imai, F. H. and R. S. Berns. High-resolution multi-spectral image archives: a hybrid approach. In Proceedings of the IS&T/SID Sixth Color Imaging Conference Color Science, Systems, and Applications, pp. 224-227. Springfield, Va.: Society for Imaging Science and Technology. 1999 Berns, R. S. Challenges for colour science in multimedia imaging systems. In Colour Imaging: Vision and Technology, eds. L. MacDonald and R. Luo, pp. 99-127. Chichester: Wiley. Burns, P. D. and R. S. Berns. Quantization in multispectral color image acquisition. In Proceedings of the IS&T/SID Seventh Color Imaging Conference: Color Science, Systems, and Applications, pp. 32-35. Springfield, Va.: Society for Imaging Science and Technology. Imai, F. H. and R. S. Berns. A comparative analysis of spectral reflectance reconstruction in various spaces using a trichromatic camera system. In Proceedings of the IS&T/SID Seventh Color Imaging Conference: Color Science, Systems, and Applications, pp. 21-25. Springfield, Va.: Society for Imaging Science and Technology. Imai, F. H. and R. S. Berns. Spectral estimation using trichromatic digital cameras. In Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, pp. 42-49. Chiba, Japan: Chiba University, Miyake Laboratory. Rosen, M. R. and X. Jiang. Lippmann 2000: A spectral image database under construction. In Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, pp. 117-122. Chiba, Japan: Chiba University, Miyake Laboratory. 2000 Berns, R. S. Billmeyer and Saltzman’s Principles of Color Technology, 3rd ed. New York:Wiley. Imai, F. H., R. S. Berns, and D. Tzeng. A comparative analysis of spectral reflectance estimation in various spaces using a trichromatic camera system. Journal of Imaging Science and Technology 44:280-287. Imai, F. H., M. R. Rosen, and R. S. Berns. Comparison of spectrally narrow-band capture versus wide-band with a priori sample analysis for spectral reflectance estimation. In Proceedings of the Eighth Color Imaging Conference: Color Science and Engineering, Systems, Technologies and Applications, pp. 234-241. Springfield, Va.: Society for Imaging Science and Technology. Imai, F. H., M. R. Rosen, R. S. Berns, N. Ohta, and N. Matsushiro. Preliminary study on spectral image compression. In Proceedings of Color Forum Japan 2000, pp. 67-70. Tokyo: Japanese Optics Society, Japanese Illumination Society, Japanese Color Society, and Japanese Photographic Society.

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis Quan, S. and N. Ohta. Optimization of camera spectral sensitivities. In Proceedings of the Eighth Color Imaging Conference: Color Science and Engineering, Systems, Technologies and Applications, pp. 273-278. Springfield, Va.: Society for Imaging Science and Technology. Rosen, M. R., M. D. Fairchild, G. M. Johnson, and D. R. Wyble. Color management within a spectral image visualization tool. In Proceedings of the Eighth Color Imaging Conference: Color Science and Engineering, Systems, Technologies and Applications, pp.75-80. Springfield, Va.: Society for Imaging Science and Technology. 2001 Berns, R. S. The science of digitizing paintings for color-accurate image archives: A review. Journal of Imaging Science and Technology 45:305-325. Imai, F. H., M. R. Rosen, and R. S. Berns. Multi-spectral imaging of a van Gogh’s self-portrait at the National Gallery of Art, Washington, D.C. In Proceedings of the IS&T PICS Conference, pp. 185-189. Springfield, Va.: Society for Imaging Science and Technology. Imai, F. H., S. Quan, M. R. Rosen, and R. S. Berns. Digital camera filter design for colorimetric and spectral accuracy. In Proceedings of the Third International Conference on Multispectral Color Science, eds. M. Hauta-Kasari, J. Hiltunen, and J. Vanhanen, pp. 13-16. Joensuu, Finland: University of Joensuu Department of Computer Science. Imai, F. H., M. R. Rosen, D. R. Wyble, R. S. Berns, and D. Tzeng. Spectral reproduction from scene to hardcopy. I: Input and Output. In Proceedings of the International Society for Optical Engineering, vol. 4306, eds. M. M. Blouke, J. Canosa , and N. Sampat, pp. 346-357. Matsushiro, N., F. H. Imai, and N. Ohta. Principal component analysis of spectral images based on the independence of color matching function vectors. In Proceedings of the Third International Conference on Multispectral Color Science, eds. M. Hauta-Kasari, J. Hiltunen, and J. Vanhanen, pp. 77-80. Joensuu, Finland: University of Joensuu Department of Computer Science. Rosen, M. R., F. H. Imai, X. Jiang, and N. Ohta. Spectral reproduction from scene to hardcopy II: Image processing. In Proceedings of the International Society for Optical Engineering, vol. 4300, eds. R. Eschbach and G. G. Marcu, pp. 33-41. 2002 Berns, R. S. and F. H. Imai. The use of multi-channel visible spectrum imaging for pigment identification. In Proceedings of the 13th Triennial ICOM-CC Meeting, pp. 217-222. London: James & James Ltd.. Berns, R. S. and R. Merrill. Color science and painting. American Artist, 68-70, 72 (January, 2002).

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis Berns, R. S. Visible-spectrum imaging techniques: An Overview. In Proceedings of the 9th Congress of the International Colour Association, Rochester, N.Y. pp. 475-480. SPIE vol. 4421. Bellingham, Wash.: The International Society for Optical Engineering. Imai, F. H. and R. S. Berns. Spectral estimation of oil paints using multi-filter trichromatic imaging. In Proceedings of the 9th Congress of the International Colour Association, Rochester, N.Y. pp. 504-507. SPIE vol. 4421. Bellingham, Wash.: The International Society for Optical Engineering. Imai, F. H., M. R. Rosen, and R. S. Berns. Comparative study of metrics for spectral match quality. In Proceedings of the First European Conference on Color in Graphics, CGIV 2002, Imaging and Vision, pp. 492-496. Springfield, Va.: Society for Imaging Science and Technology. Quan, S. and N. Ohta. Evaluating hypothetical spectral sensitivities with quality factors. Journal of Imaging Science and Technology 46:8-14. Rosen, M. R., F. H. Imai, M. D. Fairchild, and N. Ohta. Data-efficient methods applied to spectral image capture. In Proceedings of the International Congress of Imaging Science, Tokyo, ICIS’02, pp. 389-390. Tokyo: The Society of Photographic Science and Technology of Japan and The Imaging Society of Japan. Rosen, M. R., F. H. Imai, M. D. Fairchild, and N. Ohta. Data-efficient methods applied to spectral image capture. Journal of the Society of Photographic Science and Technology of Japan 65:353-362. Rosen, M. R., M. D. Fairchild, and N. Ohta. An introduction to data-efficient spectral imaging. In Proceedings of the First European Conference on Color in Graphics, CGIV’2002, Imaging and Vision, pp. 497-502. Springfield, Va.: Society for Imaging Science and Technology. 2003 Berns, R. S., L. A. Taplin, F. H. Imai, E. A. Day, D. C. Day. Spectral imaging of Matisse’s Pot of Geraniums: A case study. In Proceedings of the IS&T/SID Eleventh Color Imaging Conference: Color Science and Engineering, pp. 149-153. Springfield, Va.: Society for Imaging Science and Technology. Day, D. C. Filter selection for spectral estimation using a trichromatic camera. M.S. Thesis, Rochester Institute of Technology, Rochester, N.Y. Day, E. A. The effects of multi-channel spectrum imaging on perceived spatial image quality and color reproduction accuracy. M.S. Thesis, Rochester Institute of Technology, Rochester, N.Y. Day, E. A., R. S. Berns, L. A. Taplin, and F. H. Imai. A psychophysical experiment evaluating the color accuracy of several multispectral image capture techniques. In Proceedings of the IS&T 2003 PICS conference, pp.199-204. Springfield, Va.: Society for Imaging Science and Technology. Imai, F. H., D. R. Wyble, R. S. Berns, and D. Tzeng. A feasibility study of spectral color reproduction. Journal of Imaging Science and Technology 47: 543-553.

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis Quan, S., N. Ohta, R. S. Berns, and N. Katoh. Heirarchical approach to the optimal design of camera spectral sensitivities for colorimetric and spectral performance, pp. 159-170. SPIE 5008. Bellingham, Wash.: The International Society for Optical Engineering. Quan, S. Evaluation and optimal design of spectral sensitivities for digital color imaging. Ph.D. Dissertation, Rochester Institute of Technology, Rochester, N.Y. Rosen, M. R. Navigating the roadblocks to spectral color reproduction: Data-efficient multi-channel imaging and spectral color management. Ph.D. Dissertation, Rochester Institute of Technology, Rochester, N.Y. Sun, Q. Spectral imaging of human portraits and image quality. Ph.D. Dissertation, Rochester Institute of Technology, Rochester, N.Y. 2004 Day, E. A., R. S. Berns, L. A. Taplin, and F. H. Imai. A psychophysical experiment evaluating the color and spatial-image quality of several multi-spectral image capture techniques. Journal of Imaging Science and Technology 48:99-110. Mohammadi, M., M. Nezamabadi, R. S. Berns, and L. A. Taplin. Spectral imaging target development based on hierarchical cluster analysis. In Proceedings of the IS&T/SID Twelfth Color Imaging Conference: Color Science and Engineering: Systems, Technologies, Applications, pp. 59-64. Springfield, Va.: Society for Imaging Science and Technology. 2005 Berns, R. S., L. A. Taplin, M. Nezamabadi, Y. Zhao, and Y. Okumura. High-accuracy digital imaging of cultural heritage without visual editing. In Proceedings IS&T Second Image Archiving Conference, in press. Springfield, Va.: Society for Imaging Science and Technology. Berns, R. S., L. A. Taplin, M. Nezamabadi, and M. Mohammadi. Spectral imaging using a commercial color-filter array digital camera. In Proceedings 14th Triennial Meeting The Hague, ICOM Committee for Conservation, in press. Mohammadi, M., M. Nezamabadi, R. S. Berns, and L. A. Taplin, Pigment selection for multispectral imaging. In Proceedings 10th Congress of the International Colour Association, in press. Murphy, E. P. A testing procedure to characterize color and spatial quality of digital cameras used to image cultural heritage. M.S. Thesis, Rochester Institute of Technology, Rochester, N.Y. Rosen, M.R., and F.S. Frey. RIT American museums survey on digital imaging for direct capture of artwork. In Proceedings IS&T Second Image Archiving Conference, in press. Springfield, Va.: Society for Imaging Science and Technology.

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis Smoyer, E. P. M., L. A. Taplin, and R. S. Berns. Experimental evaluation of museum case study digital camera systems. In Proceedings IS&T Second Image Archiving Conference, in press. Springfield, Va.: Society for Imaging Science and Technology. Zhao, Y., L. A. Taplin, M. Nezamabadi, and R. S. Berns. Using matrix R method in the multispectral image archives. In Proceedings 10th Congress of the International Colour Association, in press. Technical Reports These reports can be downloaded from www.art-si.org and www.cis.rit.edu/mcsl/research/reports.shtml. 1998 Imai, F. H. Multi-spectral image acquisition and spectral reconstruction using a trichromatic digital camera system associated with absorption filters, parts I-VIII. MCSL Technical Report, August. 2000 Berns, R. S. Direct digital imaging of Vincent van Gogh’s self-portrait—A personal view. MCSL Technical Report, May. Berns, R. S. The science of digitizing two-dimensional works of art for color-accurate image archives—Concepts through practice. MCSL Technical Report, May. Imai, F. H. Spectral reproduction from scene to hardcopy: Multi-spectral acquisition and spectral estimation using a trichromatic digital camera system associated with absorption filters. Parts I and II. MCSL Technical Report, October. 2002 Berns, R. S. Phase I final report to the National Gallery of Art, Washington, Art-SI project update. MCSL Technical Report, October. Day, E. A. Colorimetric characterization of a computer-controlled (SGI) CRT display. MCSL Technical Report, April. Day, E. A., F. H. Imai, L. A. Taplin, and S. Quan. Characterization of a Roper Scientific Quantix monochrome camera. MCSL Technical Report, March. Imai, F. H. Simulation of spectral estimation of an oil-paint target under different illuminants. MCSL Technical Report, January. Imai, F. H., L. A. Taplin, and E. A. Day. Comparison of the accuracy of various transformations from multi-band images to reflectance spectra. MCSL Technical Report, Summer. Imai, F. H., L. A. Taplin, D. C. Day, E. A. Day, and R. S. Berns. Imaging at the National Gallery of Art, Washington, D.C. MCSL Technical Report, December.

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Scientific Examination of Art: Modern Techniques in Conservation and Analysis 2003 Imai, F. H., L.A., Taplin, and E. A. Day. Comparative study of spectral reflectance estimation based on broad-band imaging systems. MCSL Technical Report, April. Day, D.C. Spectral sensitivies of the Sinarback 54 camera. MCSL Technical Report, February. Day, D.C. Evaluation of optical flare and its effects on spectral estimation accuracy. MCSL Technical Report, February. 2004 Berns, R. S., L.A. Taplin, M. Nezamabadi, and Y. Zhao. Modifications of a Sinarback 54 digital camera for spectral and high-accuracy colorimetric imaging: Simulations and experiments. MCSL Technical Report, June. Mohammadi, M. and R. S. Berns. Verification of the Kubelka-Munk turbid media theory for artist acrylic paint. MCSL Technical Report, June. Mohammadi, M., M. Nezamabadi, L. A. Taplin, and R. S. Berns. Pigment selection using Kubelka-Munk turbid media theory and non-negative least squares technique. MCSL Technical Report, June. Zhao, Y., L. A. Taplin, M. Nezamabadi, and R. S. Berns, Methods of Spectral Reflectance Reconstruction for A Sinarback 54 Digital Camera. MCSL Technical Report, December.