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Multi-Temporal Image Analysis for Preventive Conservation of Historical Musical Instruments

Published:24 June 2023Publication History
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Abstract

Artworks need to be constantly monitored to check their state of conservation and to quickly spot the eventual presence of alterations or damages. Preventive conservation is the set of practices employed to reach this goal. Unfortunately, this results generally in a cumbersome process involving multiple analytical techniques. Consequently, methods able to provide a quick preliminary examination of the artworks (e.g., optical monitoring) seem very promising to streamline preventive conservation procedures. We are especially interested in the study of historical wood musical instruments, a kind of artwork particularly subject to mechanical wear since they are both held in museums and also occasionally played in concerts. Our primary goal is to detect possible altered regions on the surface of the instruments early and thus provide the experts some precise indications on where to apply more in-depth examinations to check for potential damages. In this work, we propose an optical monitoring method based on the a-contrario probabilistic framework. Tests were conducted on the “Violins UVIFL imagery” dataset, a collection of UV-induced fluorescence image sequences of artificially altered wood samples and violins. Obtained results showed the robustness of the proposed method and its capability to properly detect altered regions while rejecting noise.

REFERENCES

  1. [1] Abas Fazly Salleh and Martinez Kirk. 2003. Classification of painting cracks for content-based analysis. In Proceedings of SPIE 5011, Machine Vision Applications in Industrial Inspection XI, Vol. 5011. SPIE, 149161.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Arthur David and Vassilvitskii Sergei. 2006. k-Means++: The Advantages of Careful Seeding. Technical Report. Stanford University, Stanford, CA.Google ScholarGoogle Scholar
  3. [3] Artusi Alessandro, Banterle Francesco, and Chetverikov Dmitry. 2011. A survey of specularity removal methods. Computer Graphics Forum 30 (2011), 22082230.Google ScholarGoogle Scholar
  4. [4] Bitossi Giovanna, Giorgi Rodorico, Mauro Marcello, Salvadori Barbara, and Dei Luigi. 2005. Spectroscopic techniques in Cultural Heritage conservation: A survey. Applied Spectroscopy Reviews 40, 3 (2005), 187228. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Bradley Susan. 2005. Preventive conservation research and practice at the British Museum. Journal of the American Institute for Conservation 44, 3 (2005), 159173. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Brandmair Brigitte and Greiner Peter Stefan. 2010. Stradivari Varnish: Scientific Analysis of His Finishing Technique on Selected Instruments. Serving Audio.Google ScholarGoogle Scholar
  7. [7] Bucur Voichita. 2016. Handbook of Materials for String Musical Instruments. Springer.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Cerimele Maria Mercede and Cossu Rossella. 2007. Decay regions segmentation from color images of ancient monuments using fast marching method. Journal of Cultural Heritage 8, 2 (2007), 170175. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Cerra Daniele, Plank Simon, Lysandrou Vasiliki, and Tian Jiaojiao. 2016. Cultural Heritage sites in danger—Towards automatic damage detection from space. Remote Sensing 8, 9 (Sept.2016), 781. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Cornelis Bruno, Ruzic Tijana, Gezels Emile, Dooms Ann, Pizurica Aleksandra, Platisa Ljiljana, Cornelis Jan, Martens Maximiliaan, Mey Marc De, and Daubechies Ingrid. 2013. Crack detection and inpainting for virtual restoration of paintings: The case of the Ghent Altarpiece. Signal Processing 93, 3 (2013), 605619. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. [11] European Standard. 2011. CSN EN 16242 2011: Conservation of Cultural Heritage - Procedures and Instruments for Measuring Humidity in the Air and Moisture Exchanges Between Air and Cultural Property.European Standard.Google ScholarGoogle Scholar
  12. [12] Deborah Hilda, Richard Noel, and Hardeberg Jon Yngve. 2015. Hyperspectral crack detection in paintings. In Proceedings of the 2015 Colour and Visual Computing Symposium (CVCS’15). 16. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Desolneux Agnès, Moisan Lionel, and Morel Jean-Michel. 2000. Meaningful alignments. International Journal of Computer Vision 40, 1 (2000), 723.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] Desolneux Agnès, Moisan Lionel, and Morel J.-M.. 2003. A grouping principle and four applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 4 (2003), 508513.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Desolneux Agné, Moisan Lionel, and Morel Jean-Michel. 2004. Gestalt theory and computer vision. In Seeing, Thinking and Knowing. Springer, 71101.Google ScholarGoogle Scholar
  16. [16] Desolneux Agnes, Moisan Lionel, and Morel Jean-Michel. 2007. From Gestalt Theory to Image Analysis: A Probabilistic Approach. Vol. 34. Springer Science & Business Media.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Dondi Piercarlo, Lombardi Luca, Invernizzi Claudia, Rovetta Tommaso, Malagodi Marco, and Licchelli Maurizio. 2017. Automatic analysis of UV-induced fluorescence imagery of historical violins. Journal on Computing and Cultural Heritage 10, 2 (2017), Article 12, 13 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Dondi Piercarlo, Lombardi Luca, Malagodi Marco, and Licchelli Maurizio. 2016. Automatic identification of varnish wear on historical instruments: The case of Antonio Stradivari violins. Journal of Cultural Heritage 22 (2016), 968973. Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Dondi Piercarlo, Lombardi Luca, Malagodi Marco, and Licchelli Maurizio. 2019. Segmentation of multi-temporal UV-induced fluorescence images of historical violins. In New Trends in Image Analysis and Processing—ICIAP 2019. Lecture Notes in Computer Science, Vol. 11808. Springer, 8191.Google ScholarGoogle Scholar
  20. [20] Fichera Giusj Valentina, Albano Michela, Fiocco Giacomo, Invernizzi Claudia, Licchelli Maurizio, Malagodi Marco, and Rovetta Tomaso. 2018. Innovative monitoring plan for the preventive conservation of historical musical instruments. Studies in Conservation 63, Suppl. 1 (2018), 351354. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Fiocco Giacomo, Invernizzi Claudia, Grassi Silvia, Davit Patrizia, Albano Michela, Rovetta Tommaso, Stani Chiaramaria, et al. 2021. Reflection FTIR spectroscopy for the study of historical bowed string instruments: Invasive and non-invasive approaches. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 245 (2021), 118926. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Fiorucci Marco, Khoroshiltseva Marina, Pontil Massimiliano, Traviglia Arianna, Bue Alessio Del, and James Stuart. 2020. Machine learning for Cultural Heritage: A survey. Pattern Recognition Letters 133 (2020), 102108. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Flenner Arjuna and Hewer Gary. 2011. A Helmholtz principle approach to parameter free change detection and coherent motion using exchangeable random variables. SIAM Journal on Imaging Sciences 4, 1 (2011), 243276.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Gelli Daniele, March Riccardo, Salonia Paolo, and Vitulano Domenico. 2003. Surface analysis of stone materials integrating spatial data and computer vision techniques. Journal of Cultural Heritage 4, 2 (2003), 117125. Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Goutte Cyril and Gaussier Eric. 2005. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In Proceedings of the European Conference on Information Retrieval. 345359.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. [26] Guarneri Massimiliano, Danielis Alessandro, Francucci Massimo, Collibus Mario Ferri De, Fornetti Giorgio, and Mencattini Arianna. 2014. 3D remote colorimetry and watershed segmentation techniques for fresco and artwork decay monitoring and preservation. Journal of Archaeological Science 46 (2014), 182190. Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Hitam Muhammad Suzuri, Awalludin Ezmahamrul Afreen, Yussof Wan Nural Jawahir Hj Wan, and Bachok Zainuddin. 2013. Mixture contrast limited adaptive histogram equalization for underwater image enhancement. In Proceedings of the 2013 International Conference on Computer Applications Technology (ICCAT’13). 15. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Invernizzi Claudia, Fichera Giusj Valentina, Licchelli Maurizio, and Malagodi Marco. 2018. A non-invasive stratigraphic study by reflection FT-IR spectroscopy and UV-induced fluorescence technique: The case of historical violins. Microchemical Journal 138 (2018), 273281. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Janssens Koen and Grieken René Van. 2004. Non-Destructive Micro Analysis of Cultural Heritage Materials. Vol. 42. Elsevier.Google ScholarGoogle Scholar
  30. [30] Khelifi Lazhar and Mignotte Max. 2020. Deep learning for change detection in remote sensing images: Comprehensive review and meta-analysis. IEEE Access 8 (2020), 126385126400.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Kramer R. P., Schellen H. L., and Schijndel A. W. M. Van. 2016. Impact of ASHRAE’s museum climate classes on energy consumption and indoor climate fluctuations: Full-scale measurements in museum Hermitage Amsterdam. Energy and Buildings 130 (2016), 286294. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Li Matthew D., Chang Ken, Bearce Ben, Chang Connie Y., Huang Ambrose J., Campbell J. Peter, Brown James M., et al. 2020. Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging. NPJ Digital Medicine 3, 1 (2020), 19.Google ScholarGoogle Scholar
  33. [33] Lisani Jose Luis and Morel J.-M.. 2003. Detection of major changes in satellite images. In Proceedings of the 2003 International Conference on Image Processing, Vol. 1. IEEE, Los Alamitos, CA.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Liu Gang, Gousseau Yann, and Tupin Florence. 2019. A contrario comparison of local descriptors for change detection in very high spatial resolution satellite images of urban areas. IEEE Transactions on Geoscience and Remote Sensing 57, 6 (2019), 39043918.Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Lowe David G.. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 2 (2004), 91110.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Luo M. Ronnier, Cui Guihua, and Rigg B.. 2001. The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research & Application 26, 5 (2001), 340350.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Mandal Murari and Vipparthi Santosh Kumar. 2021. An empirical review of deep learning frameworks for change detection: Model design, experimental frameworks, challenges and research needs. IEEE Transactions on Intelligent Transportation Systems 23 (2021), 61016122.Google ScholarGoogle Scholar
  38. [38] Manferdini Anna Maria, Baroncini Valentina, and Corsi Cristiana. 2012. An integrated and automated segmentation approach to deteriorated regions recognition on 3D reality-based models of Cultural Heritage artifacts. Journal of Cultural Heritage 13, 4 (2012), 371378. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] McLachlan Geoffrey J. and Basford Kaye E.. 1988. Mixture Models: Inference and Applications to Clustering. Vol. 38. M. Dekker, New York, NY.Google ScholarGoogle Scholar
  40. [40] Müllner Daniel. 2011. Modern hierarchical, agglomerative clustering algorithms. arXiv preprint arXiv:1109.2378 (2011).Google ScholarGoogle Scholar
  41. [41] Ortiz Rocío and Ortiz Pilar. 2016. Vulnerability index: A new approach for preventive conservation of monuments. International Journal of Architectural Heritage 10, 8 (2016), 10781100. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Pizer Stephen M., Johnston R. Eugene, Ericksen James P., Yankaskas Bonnie C., and Muller Keith E.. 1990. Contrast-limited adaptive histogram equalization: Speed and effectiveness. In Proceedings of the 1st Conference on Visualization in Biomedical Computing. IEEE, Los Alamitos, CA, 337338.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Pizurica Aleksandra, Platisa Ljiljana, Ruzic Tijana, Cornelis Bruno, Dooms Ann, Martens Maximiliaan, Dubois Helene, Devolder Bart, Mey Marc De, and Daubechies Ingrid. 2015. Digital image processing of the Ghent Altarpiece: Supporting the painting’s study and conservation treatment. IEEE Signal Processing Magazine 32, 4 (2015), 112122. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Rezaee Mustafa Jahangoshai, Eshkevari Milad, Saberi Morteza, and Hussain Omar. 2021. GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game. Knowledge-Based Systems 213 (2021), 106672.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Rezaei Alireza, Aldea Emanuel, Dondi Piercarlo, Malagodi Marco, and Hégarat-Mascle Sylvie Le. 2019. Detecting alterations in historical violins with optical monitoring. In Proceedings of the 14th International Conference on Quality Control by Artificial Vision (QCAV’19). Article 1117210, 8 pages. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Rezaei Alireza, Hégarat-Mascle Sylvie Le, Aldea Emanuel, Dondi Piercarlo, and Malagodi Marco. 2021. Analysis of multi-temporal image series for the preventive conservation of varnished wooden surfaces. In Proceedings of the 16th International Symposium on Visual Computing.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. [47] Rezaei Alireza, Hégarat-Mascle Sylvie Le, Aldea Emanuel, Dondi Piercarlo, and Malagodi Marco. 2021. One step clustering based on a-contrario framework for detection of alterations in historical violins. In Proceedings of the 25th International Conference on Pattern Recognition (ICPR’20). 93489355.Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Rezaei Alireza, Hégarat-Mascle Sylvie Le, Aldea Emanuel, Dondi Piercarlo, and Malagodi Marco. 2022. A-contrario framework for detection of alterations in varnished surfaces. Journal of Visual Communication and Image Representation 83 (2022), 103357.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. [49] Robin Amandine, Moisan Lionel, and Hégarat-Mascle Sylvie Le. 2010. An a-contrario approach for subpixel change detection in satellite imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 11 (2010), 19771993.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. [50] Rousseau Francois, Faisan Sylvain, Heitz Fabrice, Armspach Jean-Paul, Chevalier Yves, Blanc Frederic, Seze Jerome de, and Rumbach Lucien. 2007. An a contrario approach for change detection in 3D multimodal images: Application to multiple sclerosis in MRI. In Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Los Alamitos, CA, 20692072.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Rovetta Tommaso, Invernizzi Claudia, Fiocco Giacomo, Albano Michela, Licchelli Maurizio, Gulmini Monica, Alf Gregg, Rombolà Alessandro, and Malagodi Marco. 2019. The case of Antonio Stradivari 1718 ex-San Lorenzo violin: History, restorations and conservation perspectives. Journal of Archaeological Science: Reports 23 (2019), 443450. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Rovetta Tommaso, Invernizzi Claudia, Licchelli Maurizio, Cacciatori Fausto, and Malagodi Marco. 2018. The elemental composition of Stradivari’s musical instruments: New results through non-invasive EDXRF analysis. X-Ray Spectrometry 47, 2 (2018), 159170. Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Santos Lorena Alves, Ferreira Karine, Picoli Michelle, Camara Gilberto, Zurita-Milla Raul, and Augustijn Ellen-Wien. 2021. Identifying spatiotemporal patterns in land use and cover samples from satellite image time series. Remote Sensing 13, 5 (2021), 974.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Stovel Herb. 1998. Risk Preparedness: A Management Manual for World Cultural Heritage. ICCROM.Google ScholarGoogle Scholar
  55. [55] Stuart Barbara H.. 2007. Analytical Techniques in Materials Conservation. John Wiley & Sons.Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Sturari Mirco, Paolanti Marina, Frontoni Emanuele, Mancini Adriano, and Zingaretti Primo. 2017. Robotic platform for deep change detection for rail safety and security. In Proceedings of the 2017 European Conference on Mobile Robots (ECMR’17). IEEE, Los Alamitos, CA, 16.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Garry Thomson (Ed.). 1968. Contributions to the London Conference on Museum Climatology, 18-23 September 1967. International Institute for Conservation of Historic and Artistic Works.Google ScholarGoogle Scholar
  58. [58] Torr Philip H. S. and Zisserman Andrew. 2000. MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78, 1 (2000), 138156.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. [59] Varghese Ashley, Gubbi Jayavardhana, Ramaswamy Akshaya, and Balamuralidhar P.. 2018. ChangeNet: A deep learning architecture for visual change detection. In Proceedings of the European Conference on Computer Vision Workshops (ECCV’18 Workshops).Google ScholarGoogle Scholar
  60. [60] Verma Sagar, Panigrahi Akash, and Gupta Siddharth. 2021. QFabric: Multi-task change detection dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10521061.Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Wang Zhiqiang, Yu Zhiwen, Chen C. L. Philip, You Jane, Gu Tianlong, Wong Hau-San, and Zhang Jun. 2017. Clustering by local gravitation. IEEE Transactions on Cybernetics 48, 5 (2017), 13831396.Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Wirilander Heidi. 2012. Preventive conservation: A key method to ensure Cultural Heritage’s authenticity and integrity in preservation process. E-conservation Magazine 6, 24 (2012), 165176.Google ScholarGoogle Scholar
  63. [63] Yang Jie, Gong Dong, Liu Lingqiao, and Shi Qinfeng. 2018. Seeing deeply and bidirectionally: A deep learning approach for single image reflection removal. In Proceedings of the European Conference on Computer Vision (ECCV’18). 654669.Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. [64] Zhang Zheng, Tang Ping, and Corpetti Thomas. 2016. Satellite image time series clustering via affinity propagation. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’16). 24192422. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  65. [65] Zhu Xiatian, Loy Chen Change, and Gong Shaogang. 2014. Constructing robust affinity graphs for spectral clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 14501457.Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. [66] Zhu Zhe. 2017. Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing 130 (2017), 370384.Google ScholarGoogle ScholarCross RefCross Ref

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        cover image Journal on Computing and Cultural Heritage
        Journal on Computing and Cultural Heritage   Volume 16, Issue 2
        June 2023
        312 pages
        ISSN:1556-4673
        EISSN:1556-4711
        DOI:10.1145/3585396
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        Publication History

        • Published: 24 June 2023
        • Online AM: 6 May 2023
        • Accepted: 21 September 2022
        • Revised: 28 August 2022
        • Received: 24 December 2021
        Published in jocch Volume 16, Issue 2

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