Bridging Between Cultural Heritage Institutions: 9th Italian by Paola Manoni (auth.), Tiziana Catarci, Nicola Ferro,

By Paola Manoni (auth.), Tiziana Catarci, Nicola Ferro, Antonella Poggi (eds.)

This publication constitutes the completely refereed complaints of the ninth Italian learn convention on electronic Libraries, held in Rome, Italy, in January/February 2013. The 18 complete papers provided including an invited paper and a panel paper have been chosen from prolonged types of the displays given on the convention. The papers then went via an extra around of reviewing and revision after the development. The papers are prepared in topical sections on info entry; electronic Library (DL) structure; DL tasks; semantics and DLs; types and review for DLs; DL purposes; discussing DL perspectives.

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Fig. 3. A picture involving a combination of several shapes the samples considered at each next step are a superset of those in the previous one, and hence the number of new comparisons per shape is, respectively, 4096, 8192, 16384 and 32768 in the last step. 5 Prospects The final aim of this kind of processing, that we intend to develop as future work, is to be able to understand the content of a whole picture based on the shapes recognized in it and to the particular spatial relationships existing among them.

According to the definition reported in the 1972 UNESCO “World Heritage Convention” - Article 1 - cultural heritage refers to “monuments”, “groups of buildings” and “sites” which are of outstanding universal value historically, artistically or scientifically. However, the concept of cultural heritage has recently assumed a broader connotation and includes, among other things, tangible, moveable objects such as works of art, artifacts, scientific specimens, photographs, books, manuscripts and recorded moving image and sound [2].

Several pixels, taken together, may make up more significant items such as lines, contours, blobs, textures. e. functions operating on pixels that enhance some important details and/or dim other, less significant ones, such as the noise introduced by the acquisition means or by the representation format (if lossy). Step 2 identifies and extracts significant information from the pre-processed image resulting from (a combination of) the aforementioned techniques. The information obtained in this way allows for a higher-level interpretation of the image.

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