Mapping the Conservations Status of Easel Painting. Craquelure Structure Visualization by Binary Image Segmentation Approach


  • Magdelena Stoyanova Ca’ Foscari University, Centro Interdisciplinare di Studi Balcanici ed Internazionali, Venice, Italy
  • Tibor Lukić Department of Mathematics, Faculty of Technical Sciences, University of Novi Sad, Serbia



Mapping the Conservation Status, Spectral Imaging Techniques, Integral Analysis, Binary Image Segmentation


In easel painting (icons) mapping the status of conservation (MCS) is a complex process of prospecting in various spectral, graphic, and spatial techniques, integral analysis and “technical” visualizations of the data whose function is to lay out detected damages or symptoms, not directly perceivable from the VIS, UV, IR or X-r imaging. In this work, the results of a multimethodological survey applied to the end of detecting sharp discontinuities (boundary of cavities, wrinkles and fractures in the host medium) are illustrated. As, due to the microscopic size and depth of a crackle/wrinkle fragment or net, it may be rather difficult to single out its position and extent because of the generally low signal-to-noise (S/N) ratio, binary image segmentation and highresolution data acquisition has been adopted for improving the visibility of the studied area.


Stoyanova, M. et al. Spectral Investigation of Serbian Baroque Icons for their Scientific Documentation. (Technical Report) In: Art. Conservation. Restoration of easel tempera painting (3/07/ 2015), see DOI: 10.13140/RG.2.1.3562.6087.

Bentchev I. Naturwissenschaftliche Methoden bei der Untersuchung von Ikonen — Exemplarisch dargestellt an der „Gottesmutter mit Kind” der Prinz Johann Georg Sammlung // Hermeneia. Zeitschrift für ostkirchliche Kunst. — 1986. — Heft 2. Bochum, 1986 .

Stoyanova, M. Towards a more efficient use of spectral techniques in the attribution of easel painting.” Conference presentation (COSCH 4 th Workshop & Working Group Meeting, Belgrade (Serbia) 16/09/2014).

Grenberg, Ju. Technology and investigation of easel and wall painting. Moscow 2000 (Технология и исследование произведений станковой и настенной живописи. Москва: ГОСНИИР 2000).

Lelekova, Olga. “Naturwissenschaftliche Methoden zur Aufdeckung von Ikonenfälschungen” In: Ikonen. Restaurierung und naturwissenschaftiliche Erforschung. Beiträge des internationalen Kolloquiums in Recklinghausen 1994. Herausgeber Ivan Bentschev und Eva Haustein-Bartsch. München (Editio Maris) 1997, 35-43.

Buseva-Davydova, Ja.L. “Old Believers‘ fakes in icon art: problems of iudentification”. Moscow 2008 (Я. Л. Бусева - Давыдова. Старообрядческие подделки в иконописи: проблема идентификации, B : Иконопись Мстёры: История, Структура Промысла, Художественные Особенности. Москва 2008).

Krasilin, M. The Russian icon between 18 th and 19 th cc. (Красилин М. М. Русская икона XVIII начала XX веков // История иконописи. Истоки. Традиции. Современность. VI - XX века. - Москва 2002, 211 -230. ) Moscow 2002.

Bentschev, I. „Zu einer Restaurierungssignatur des altgläubigen Ikonenmalers Nikita Sevast ́janović Raćejskij von 1872 // In: Ikonen. Restaurierung und naturwissenschaftiliche Erforschung, 115-124.

Vzdornov, G.I. The discovery and study of the Russian mediaeval painting. The 19th century. (In Russian) Moscow 1986.

Papers presented by T. Mossunova and V. Baranov/I. Kochetkov at the “Expertize and Attribution” Conference, Moscow 2005.

Baranov, V.V. The icon painting of Mstera: history, branch structure, artistic characteristics.(B.B.Баранов. Иконопись Мстеры: история, структура промысла, художественные особенности ) St. Peterburg 2008.

Filatov, V.V. Restoration of easel tempera painting (in Russian). Moscow 1986.

W. Zhang, Z. Zhang, D. Qi and Y. Liu. Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring, Sensors, vol. 12, pp. 19307-19328, 2014.

C. Fang, L. Zhe and Y. Li. Images Crack Detection Technology based on Improved Kmeans Algorithm, Journal of Multimedia, vol. 9, no. 6, 2014.

H.-G. Moon and J.H. Kim. Intelligent crack detecting algorithm on the concrete crack image using neural network, Proceedings of the 31st International Association for Automation and Robotics in Construction (IAARC), Sydney, Australia, 2014.




How to Cite

Stoyanova, M., & Lukić, T. (2015). Mapping the Conservations Status of Easel Painting. Craquelure Structure Visualization by Binary Image Segmentation Approach. Digital Presentation and Preservation of Cultural and Scientific Heritage, 5, 141–155.

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