Data mining approach to Image feature extraction in old painting restoration

• Autorzy: Gancarczyk J. , Sobczyk J.
• Języki publikacji: EN

Abstrakty

EN

In this paper a new approach to image segmentation was discussed. A model based on a data mining algorithm set on a pixel level of an image was introduced and implemented to solve the task of identification of craquelure and retouch traces in digital images of artworks. Both craquelure and retouch identification are important steps in art restoration process. Since the main goal is to classify and understand the cause of damage, as well as to forecast its further enlargement, a proper tool for a precise detection of the damaged area is needed. However, the complex nature of the pattern is a reason why a simple, universal detection algorithm is not always possible to be implemented. Algorithms presented in this work apply mining structures which depend of expandable set of attributes forming a feature vector, and thus offer an elastic structure for analysis. The result obtained by our method in craquelure segmentation was improved comparing to the results achieved by mathematical morphology methods, which was confirmed by a qualitative analysis.

Słowa kluczowe

EN

data mining application; image processing; k-means clustering; decision tree based image segmentation; virtual restoration of paintings

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2013
• Tom: Vol. 38, No. 3
• Strony: 159--174
• Opis fizyczny: Bibliogr. 46 poz., fig.

Twórcy

autor

Gancarczyk J.

• University of Bielsko-Biala Department of Mechanics, Willowa 2, 43-309, Bielsko-Biala, Poland

autor

Sobczyk J.

• National Museum in Krakow, Laboratory of Analysis and Non-Destructive Investigation of Her- itage Objects, Pilsudskiego 14, 31-106, Krakow, Poland

Bibliografia

• [1] Abas F.S. (2004) Analysis of Craquelure Patterns for Content-Based Retrieval. PhD Thesis, University of Southampton, Southampton
• [2] Abas F.S., Martinez K. (2003) Classifcation of painting cracks for content-based analysis. IST/SPIE's 15th Annual Symp Electronic Imaging, Santa Clara, California, USA
• [3] Abas F.S., Martinez K. (2002) Craquelure analysis for content-based retrieval. Proc of 14th Int Conf on Dig Sig Proc, Santorini, Greece, 111-114
• [4] Addis M., Lewis P., Martinez K. (2002) ARTISTE image retrieval system puts European galleries in the picture, Cultivate Interactive,7, http://www.cultivateint. org/issue7/artiste
• [5] Barni M., Bartolini F., Cappellini V. (2000) Image processing for virtual restoration of artworks. IEEE Multimedia, 7,2:34-37
• [6] Barni M., Pelagotti A., Piva A. (2005) Image processing for the analysis and conservation of paintings: opportunities and challenges, IEEE Sig Proc Mag, 141
• [7] Berns R. S., Byrns S., Casadio F., Fiedler I., Gallagher C., Imai F. H., Newman A., Taplin L. A. (2006) Rejuvenating the color palette of George Seurats A Sunday on La Grande Jatte1884: A simulation. Color Research and Application 31:278293
• [8] Berezhnoy I., Postma E. O., van den Herik H. J. (2007) Computer analysis of van goghs coplementary colours. Pattern Recognition Letters, 28:703709
• [9] Beucher S., Lantujoul C. (1979) Use of watersheds in contour detection. Proc. Int workshop on im proc, real-time edge and motion detection
• [10] Bucklow S.L. (1998) A sylometric analysis of Craquelure. Computers and the Humanities 31:503-521
• [11] Canny J. (1986) A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714
• [12] Cappellini V., Barni M., Corsini M., de Rosa A., Piva A. (2003) ArtShop: an art-oriented image-processing tool for cultural heritage applications. J Visual Comput Animat 14:149-158
• [13] Cappellini V., Piva A. (2006) Opportunities and Issues of image processing for cultural heritage applications, Proc EUSIPCO 2006, Florence, Italy
• [14] Dik J., Janssens K., Van Der Snickt G., van der Loeff., Rickers K., Cotte M. (2008) Visualization of a lost painting by Vincent van Gogh using synchrotron radiation based X-ray uorescence elemental mapping. Analytical Chemistry, 80(16):64366442
• [15] Fox M.D., Frizzell L.A., Franks L.A., Darken L.S., James R.B. (2000) Medical Imaging. The Electrical Engineering Handbook (ed. Dorf R.C.), CRC Press LLC, Boca Raton
• [16] Gonzalez R.C., Woods R. (2007) Digital Image Processing, 3rd Edition, Prentice Hall
• [17] Gupta A., Khandelwal V., Gupta A., Srivastava M.C. (2008) Image processing methods for the restoration of digitized paintings. Thammasat Int J Sc Tech 13,3:66-72
• [18] Hanbury A., Kammerer P., Zolda E. (2003) Painting crack elimination using viscous morphological reconstruction. Proc ICIAP'03 Mantova, Italy
• [19] Heckerman D., Geiger D., Chickering D. M. (1995) Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning 20(3), pp. 197-243, DOI:10.1007/BF00994016]
• [20] Jacobsen C.R. (2012) Digital Painting Analysis. Authentication and Artistic Style from Digital Reproductions. PhD thesis, Department of Mathematical Sciences, Aalborg University, Aalborg
• [21] Johnson C. R. Jr., Hendriks E., Berezhnoy I., Brevdo E., Hughes S. M., Daubechies I., Li J., Postma E., Wang J. Z. (2008) Image processing for artist identifcation. IEEE Signal Processing Magazine (39)
• [22] Johnson C.R., Hendriks E. (2007) Brushwork in the Paintings of Vincent Van Gogh, Proc. 1st IP4AI Workshop, May 2007, Amsterdam, the Netherlands
• [23] Lettner M., Sablatnig R. (2009) Spatial and spectral based segmentation of text in multispectral images of ancient documents. Proc. 10th International Conference on Document Analysis and Recognition (ICDAR 2009), pp. 813817, Barcelona, Spain
• [24] Lettner M., Diem M., Sablatnig R., Miklas H. (2008) Registration and enhancing of multispectral manuscript images. Proc. EUSIPCO08, Lausanne, Switzerland
• [25] Liu J., Lu D. (2008) Knowledge based lacunas detection and segmentation for ancient paintings, Proc. of the 13th Int. Conf. on Virtual Systems and Multimedia, 121-131
• [26] Lizun D. Fine Art Conservation, http://fneartconservation.ie/damian-lizunfne-art-conservation-4-4-43.html
• [27] Martinez K., Goodal S. (2008) Colour cluster analysis for pigment identifcation. Proc. SPIE Electronic Imaging: Computer Image Analysis in the Study of Art, San Jose, USA
• [28] MSDN: Microsoft Clustering Algorithm Technical Reference, http://msdn.microsoft.com/en-us/library/cc280445 (2013)
• [29] MSDN: Microsoft Decision Trees Algorithm Technical Reference, http://msdn.microsoft.com/en-us/library/cc645868 (2013)
• [30] Pappas M., Pitas I. (2000) Digital Color Restoration of Old Paintings, IEEE Transactions on Image Processing, 9(2), pp.291-294
• [31] Renna F., Mosca N., Carlomagno G., Attolico G., Distante A. (2005) An innovative aided virtual aproach to the recomposition of fragments. Proc. 22nd International Symposium on Automation and Robotics in Construction, pp. 16, Ferrara, Italy
• [32] Serra J. (1982) Image Analysis and Mathematical Morphology Vol. I, Ac. Press, London
• [33] Shahram M., Stork D. G., Donoho D. (2008) Recovering layers of brush strokes through statistical analysis of color and shape: an application to van goghs self portrait with grey felt hat. Comp. Im. Analysis in the Study of Art (6810)
• [34] Sobczyk J. (2008), Computer Image Analysis as a Method to Calculate the Original Surface Area of the Banner, in: Ivan Mazepas Hetmans Banner (Conservation Work at the National Museum in Krakow), pp. 216-217, Krakow
• [35] Sobczyk J., Obara B., Fraczek P., Sobczyk J. (2006) The application of image analysis in non-invasive research of historical objects. Selected examples. [in Polish: Zastosowania analizy obrazu w nieniszczacych badaniach obiektow zabytkowych. Wybrane przyklady], Ochrona zabytkow, 2:69-78
• [36] Stanco F., Battiato S., Gallo G. (2011) Digital Imaging for Cultural Heritage Preservation: Analysis, Restoration, and Reconstruction of Ancient Artworks. CRC Press
• [37] Stergiopoulos S. (ed.) (2000) Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real-Time Systems. CRC Press LLC, Boca Raton, Florida
• [38] Stork D.G. (2004) Optics and realism in Renaissance art. Scientifc American 291,6:76-83
• [39] Stork D.G. (2009) Computer vision and computer graphics analysis of paintings and drawings: An introduction to the literature. Proc 13th Int Conf on Computer Analysis of Images and Patterns, 9-24
• [40] Stout G.L. (1977) A trial index of laminal disruption. JAIC 17,1,3:17-26
• [41] Szabo T.L. (2004) Diagnostic Ultrasound Imaging: Inside Out. Academic Press series in Biomedical Engineering, Elsevier Academic Press
• [42] Szeliski R. (2011) Computer Vision. Algorithms and Applications. Springer- Verlag, London
• [43] Tadeusiewicz R., Korohoda P. (1997) Computer Analysis and Image Processing [in Polish: Komputerowa analiza i przetwarzanie obrazow], Progress of Telecommunication Foundation Publishing House, Krakow
• [44] Toler-Franklin C., Brown B., Weyrich T., Funkhouser T. Rusinkiewicz S. (2010) Multi-feature matching of fresco fragments. ACM Transactions on Graphics (TOG) - Proc. ACM SIGGRAPH Asia 29(6)
• [45] Ünsalan C., Boyer K.L. (2011) Multispectral Satellite Image Understanding. From Land Classifcation to Building and Road Detection, Springer-Verlag London, London
• [46] De Willigen P. (1999) A Mathematical Study on Craquelure and other Mechanical Damage in Paintings. Delft University Press, Delft