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Proceedings Paper

A framework for analysis of large database of old art paintings
Author(s): Jérome Da Rugna; Gaël Chareyron; Ruven Pillay; Morwena Joly
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Paper Abstract

For many years, a lot of museums and countries organize the high definition digitalization of their own collections. In consequence, they generate massive data for each object. In this paper, we only focus on art painting collections. Nevertheless, we faced a very large database with heterogeneous data. Indeed, image collection includes very old and recent scans of negative photos, digital photos, multi and hyper spectral acquisitions, X-ray acquisition, and also front, back and lateral photos. Moreover, we have noted that art paintings suffer from much degradation: crack, softening, artifact, human damages and, overtime corruption. Considering that, it appears necessary to develop specific approaches and methods dedicated to digital art painting analysis. Consequently, this paper presents a complete framework to evaluate, compare and benchmark devoted to image processing algorithms.

Paper Details

Date Published: 8 March 2011
PDF: 8 pages
Proc. SPIE 7869, Computer Vision and Image Analysis of Art II, 786907 (8 March 2011); doi: 10.1117/12.872500
Show Author Affiliations
Jérome Da Rugna, Pôle Univ. Lonard de Vinci (France)
Gaël Chareyron, Pôle Univ. Lonard de Vinci (France)
Ruven Pillay, C2RMF (France)
Morwena Joly, C2RMF (France)

Published in SPIE Proceedings Vol. 7869:
Computer Vision and Image Analysis of Art II
David G. Stork; Jim Coddington; Anna Bentkowska-Kafel, Editor(s)

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