Auflistung nach Schlagwort "alignment"
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- ZeitschriftenartikelThe CITE architecture (CTS/CITE) for analysis and alignment(it - Information Technology: Vol. 62, No. 2, 2020) Blackwell, Christopher W.; Smith, NeelDocumenting text-reuse (when one text includes a quotation or paraphrase of, or even allusion to another text) is one example of the problem of analysis and alignment . The most clever analytical tools will be of no avail unless their results can be cited , as scholarly evidence has been cited for centuries. This is where the CITE Architecture can help. CITE solves several problems at once. The first problem is the endless possible number of analyses (by which we mean “desirable ways of splitting up a text”): do we choose to “read” a text passage-by-passage, clause-by-clause, word-by-word, or syllable-by-syllable? The second, related to the first, is that of overlapping hierarchies: The first two words of the Iliad are “μῆνιν ἄειδε,” but the first metrical foot of the poem is “μηνιν α”; the first noun-phrase is “μῆνιν οὐλομένενην”, the first word of the first line, and the first word of the second line, and nothing inin between . All of these issues are present when documenting text-reuse, and especially when documenting different (and perhaps contradictory) scholarly assertions of text-reuse. In our experience, over 25 years of computational textual analysis, no other technological standard can address this problem as easily.
- KonferenzbeitragUnsupervised Learning of Fingerprint Rotations(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Schuch, Patrick; May, Jan Marek; Busch, ChristophThe alignment of fingerprint samples is a preprocessing step in fingerprint recognition. It allows an improved biometric feature extraction and a more accurate biometric comparison. We propose to use Convolutional Neural Networks for estimation of the rotational part. The main contribution is an unsupervised training strategy similar to Siamese Networks for estimation of rotations. The approach does not need any labelled data for training. It is trained to estimate orientation differences for pairs of samples. Our approach achieves an alignment accuracy with a mean absolute deviation 2:1 on data similar to the training data, which supports the alignment task. For other datasets accuracies down to 6:2 mean absolute deviation are achieved.