Ontology Learning for the Semantic Web
Ontology Learning for the Semantic Web
Image Processing for Virtual Restoration of Artworks
IEEE MultiMedia
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This work aims to deal with the problem of identifying, analysing, and classifying cracks in figurative works. The recent, considerable progress made in techniques for processing visual information has broadened the number of scientific applications in which the display and graphic processing of data play a fundamental role. Cracks consist of many elements; distinguishing and studying them has made it possible to develop a classification that in some cases can also be used as tool to verify a work's authenticity. RESTART, the system presented here, is deemed suitable for use in numerous and varied settings, such as teaching, conservation, study and research. It is able to investigate, study, research and 'restore' the digital, in accordance with the criteria dictated by knowledge. Also of considerable interest is the investigation of a crack based on such characteristics as origin and pathology, and the possibility of analysing the cracks in a fresco. The use of RESTART for such case examples is investigated and proposed.