Abstract
This paper presents image acquisition and readability enhancement techniques based on multispectral imaging. In an interdisciplinary manuscript and palimpsest research project an imaging system using a combination of LED illumination and spectral filtering was developed. On basis of the resulting multispectral image information the readability of the texts is enhanced and palimpsest texts are made visible by applying two different methods of Blind Source Separation, namely Principal Component Analysis and Independent Component Analysis.
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Bianco, G., Bruno, F., Salerno, E., Tonazzini, A.: Quality Improvement of Multispectral Images for Ancient Document Analysis. In: Proc. EuroMed 2010, 3rd International Conference Dedicated on Digital Heritage, pp. 29–34 (2010)
Brauers, J., Schulte, N., Aach, T.: Multispectral Filter-Wheel Cameras: Geometric Distortion Model and Compensation Algorithms. IEEE Transactions on Image Processing 17(12), 2368–2380 (2008)
Christens-Barry, W.A.: LED Imaging of the Archimedes palimpsest, http://archimedespalimpsest.org/imaging_experimental3.html (accessed 2012)
Easton, R., Christens-Barry, W., Knox, K.: Spectral Image Processing and Analysis of the Archimedes Palimpsest. In: 19th European Signal Processing Conference, EUSIPCO 2011 (2011)
Easton, R., Knox, K., Christens-Barry, W.: Multispectral Imaging of the Archimedes Palimpsest. In: 32nd Applied Image Pattern Recognition Workshop, AIPR 2003, pp. 111–118. IEEE Computer Society, Washington, DC (2003)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, Inc. (2001)
Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Networks 13(4-5), 411–430 (2000)
Kim, S.J., Deng, F., Brown, M.S.: Visual enhancement of old documents with hyperspectral imaging. Pattern Recognition 44(7), 1461–1469 (2011)
Lettner, M., Diem, M., Sablatnig, R., Miklas, H.: Registration of Multispectral Manuscript Images as Prerequisite for Computer Aided Script Description. In: 12th Computer Vision Winter Workshop, St.Lambrecht, Austria (2007)
Lettner, M., Diem, M., Sablatnig, R., Miklas, H.: Registration and Enhancing of Multispectral Manuscript Images. In: 16th European Signal Processing Conference, EUSIPCO 2008 (2008)
Liang, H.: Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Applied Physics A: Materials Science & Processing 106, 309–323 (2012)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mairinger, F.: Strahlenuntersuchung an Kunstwerken. E.A. Seemann, Berlin (2003)
Rapantzikos, K., Balas, C.: Hyperspectral imaging: potential in non-destructive analysis of palimpsests. In: IEEE International Conference on Image Processing, September 11-14, vol. 2, pp. 618–621 (2011)
Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis: An Introduction. Springer-Verlag New York, Inc., Secaucus (2005)
Salerno, E., Tonazzini, A., Bedini, L.: Digital image analysis to enhance underwritten text in the Archimedes palimpsest. International Journal on Document Analysis and Recognition 9(2), 79–87 (2007)
Wartenberg, D.: Multivariate Spatial Correlation: A Method for Exploratory Geographical Analysis. Geographical Analysis 17(4), 263–283 (1985)
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Hollaus, F., Gau, M., Sablatnig, R. (2012). Multispectral Image Acquisition of Ancient Manuscripts. In: Ioannides, M., Fritsch, D., Leissner, J., Davies, R., Remondino, F., Caffo, R. (eds) Progress in Cultural Heritage Preservation. EuroMed 2012. Lecture Notes in Computer Science, vol 7616. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34234-9_4
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DOI: https://doi.org/10.1007/978-3-642-34234-9_4
Publisher Name: Springer, Berlin, Heidelberg
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