By Paola Manoni (auth.), Tiziana Catarci, Nicola Ferro, Antonella Poggi (eds.)
This ebook 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 offered including an invited paper and a panel paper have been chosen from prolonged models of the shows given on the convention. The papers then went via an extra around of reviewing and revision after the development. The papers are equipped in topical sections on details entry; electronic Library (DL) structure; DL initiatives; semantics and DLs; versions and evaluate for DLs; DL functions; discussing DL perspectives.
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Additional info for Bridging Between Cultural Heritage Institutions: 9th Italian Research Conference, IRCDL 2013, Rome, Italy, January 31–February 1, 2013, Revised Selected Papers
2 Feature Extraction / Representation Given a blob, the associated shape is a more reﬁned description ready to be compared to the available models (expressed in the form of shapes, as well). Blob border was found to be a very indicative feature for object recognition . Speciﬁcally, Fourier descriptors based on distance from the centroid of the shape to its contour points proved to be very eﬀective. Thus, we were inspired by this indicator to set up our approach. Diﬀerently from Fourier descriptors, however, we consider the distance from the centroid for contour pixels, and pick just those intersecting selected radian lines at pre-deﬁned angles, originated in the shape centroid.
We compare their histograms, representing the distance from the centroid of the blob border in each of the radian directions, according to the intuition that, the more deformed is an object with respect to the model, the more diﬀerent they are. Speciﬁcally, we proceed by overlapping them and summing the absolute pairwise diﬀerences of corresponding bars to obtain the overall evaluation (in this case, representing a distance). Another option might be using the statistical measure of variance, but since in our case both the number of values and the values are normalized, a simple summation provides the same results with much less eﬀort.