MOSAIC+: Fragment retrieval and reconstruction enhancement for virtual restoration. (December 2015)

• Record Type: Journal Article
• Authors: Riccio, Daniel; Caggiano, Sonia; De Marsico, Maria; Distasi, Riccardo; Nappi, Michele
• Is Part Of: Journal of visual languages & computing. Volume 31:Part B(2016)
• Page Start: 139
• Page End: 149
• Publication Date: 2015-12
• Subjects: Artwork fragmentation -- Virtual restoration -- Craquelure -- Color indexing -- Shape indexing -- Inpainting; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2015.10.010
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: When a piece of art from the past is found, it is often broken into several fragments. This is a very common case with frescoes and pottery. Reconstruction from these fragments requires human expertize and it is almost always very hard, if not impossible, to be completely automated. Actually, the problem is an example of jigsaw puzzle solving, which is known to be NP-complete from a computational point of view. The possible high number of fragments and their possible fragility make the task formidable. This work describes software tools that help in two ways. First, reconstruction is assisted by a content-based database of the available pieces. Once acquired by suitable photo equipment and suitably annotated, the fragments can be left untouched and manipulated virtually to find out the best combination before proceeding with the actual reconstruction, if called for. Second, the unavoidable gaps and cracks in the reconstruction are filled by an inpainting module. The results have been assessed by running the system on both artificial datasets and actual case studies.