Digital reintegration of distributed mural paintings at different architectural phases: the case of St. Quirze de Pedret

Sant Quirze de Pedret is a Romanesque church located in Cercs (Catalonia, Spain) at the foothills of the Pyrenees. Its walls harbored one of the most important examples of mural paintings in Catalan Romanesque Art. However, in two different campaigns (in 1921 and 1937) the paintings were removed using the strappo technique and transferred to museums for safekeeping. This detachment protected the paintings from being sold in the art market, but at the price of breaking the integrity of the monument. Nowadays, the paintings are exhibited in the Museu Nacional d'Art de Catalunya - MNAC (Barcelona, Catalonia) and the Museu Diocesà i Comarcal de Solsona - MDCS (Solsona, Catalonia). Some fragments of the paintings are still on the walls of the church. In this work, we present the methodology to digitally reconstruct the church building at its different phases and group the dispersed paintings in a single virtual church, commissioned by the MDCS. We have combined 3D reconstruction (LIDAR and photogrammetric using portable artificial illumination) and modeling techniques (including texture transfer between different shapes) to recover the integrity of the monument in a single 3D virtual model. Furthermore, we have reconstructed the church building at different significant historical moments and placed actual paintings on its virtual walls, based on archaeological knowledge. This set of 3D models allows experts and visitors to better understand the monument as a whole, the relations between the different paintings, and its evolution over time.The project has been promoted by the Museu Diocesà i Comarcal de Solsona (special thanks are due to Carles Freixes and Lídia Fàbregas), co-financed by "La Caixa" Foundation and the Department of Culture of the Generalitat de Catalunya. Likewise, the work presented here counted with the support of MEIC (Spanish Government) project 3D4LIFE (TIN-2017-88515-C2), PRECA II of the Universitat de Barcelona (HAR2017-84451-P) and EHEM (JPICH0127 Conservation, Protection and Use, PCI2020-111979). The high-resolution photographs of the paintings are by Gaetano Alfano (Università degli Studi della Tuscia).Peer ReviewedPostprint (published version

Spartan Daily, November 13, 2014

Volume 143, Issue 32https://scholarworks.sjsu.edu/spartandaily/1531/thumbnail.jp

Arduino-controlled Reflectance Transformation Imaging to the study of cultural heritage objects

Fundacao para a Ciencia e a Tecnologia, Portugal (Grant Nos. UIDB/04349/2020 and UID/FIS/04559/2019)- Private funds. V.C. acknowledges the support from UID/Multi/04349/2019. J.C. acknowledges NOVA.ID.FCT.This article examines the development of a low-cost and portable set-up controlled by an Arduino board to perform Reflectance Transformation Imaging technique, from the information derived from 45 digital photographs of an object acquired using a stationary camera. The set-up consists of 45 high-intensity light emitting diodes (LEDs) distributed over a hemispherical dome of 70 cm in diameter and a digital camera on the top of the dome. The LEDs are controlled by an Arduino board, and the user can individually control the LEDs state (ON or OFF) and duration of illumination. An old manuscript written with iron-gall ink and a set of 1 Euro coins mint in 2002 were photographed with the set-up. The interactive re-lighting and the mathematical enhancement of the object's surface revealed corrosion, loss of material, scratches and other details, which were not perceived in standard images. These unique features, which can be extracted using edge detection processing, have immediate application in different fields such as cultural heritage or forensic studies, where they can be used as fingerprints to identify unique objects, allowing also recognizing the use of tools to alter the surface of coins to increase the price in the market.publishersversionpublishe

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Visual complexity modelling based on image features fusion of multiple kernels

[Abstract] Humans’ perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf’s law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans’ perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.Xunta de Galicia; GRC2014/049Portuguese Foundation for Science and Technology; SBIRC; PTDC/EIA EIA/115667/2009Xunta de Galicia; Ref. XUGA-PGIDIT-10TIC105008-PRMinisterio de Ciencia y Tecnología; TIN2008-06562/TINMinisterio de Ecnomía y Competitividad; FJCI-2015-2607