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This ebook constitutes the refereed complaints of the second one overseas Workshop, IWCF 2008, held in Washington, DC, united states, August 2008.
The 19 revised complete papers awarded have been conscientiously reviewed and chosen from 39 submissions. The papers are prepared in topical sections on tendencies and demanding situations; scanner, printer, and prints; human identity; shoeprints; linguistics;decision making and seek; speech research; signatures and handwriting.
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Extra resources for Computational Forensics: Second International Workshop, IWCF 2008, Washington, DC, USA, August 7-8, 2008. Proceedings
11]  traces documents according to the printing device by extracting graylevel co-occurrence features from the printed letter “e”. But their method is based on 1200dpi high resolution scans and consequently also not feasible for high throughput systems. The systems outlined by Tchan exhibit high similarity to our approach. He captured documents with a camera at low resolution and diﬀerentiates printing technologies by measuring edge sharpness, surface roughness and image contrast. However, experimental results so far were shown for documents containing simple squares and circles but have not been tested on oﬃce documents.
Image based printing technique recognition. Project Thesis (May 2006) 21. : A threshold selection method from gray level histograms. IEEE Transactions on Systems, Man and Cybernetics (9), 62–66 (1979) 22. : Digital Image Processing, 3rd edn. Prentice Hall International, Englewood Cliﬀs (2007) 23. 5: programs for machine learning. Morgan Kaufmann Publishers Inc, San Francisco (1993) 24. : Learning Internal Representations by Error Propagation. L. ) Parallel Distributed Processing: Explorations in the Microstructure of Cognition.
Since this feature is not among the top 3 pca features, the accuracy for this resolution drops down a lot (Fig. 7). Regarding results at 400dpi the top 3 features improved the accuracy rates signiﬁcantly in comparison to experiments including all features. 44 C. Schulze et al. Single feature comparison with MLP 100 area diff cooc gauss cooc lbp corr coeff edge dist edge rough gray dist all 90 Accuracy rate [%] 80 70 60 50 40 100 150 200 250 Resolution [dpi] 300 350 400 Fig. 6. Accuracy rate for classiﬁcation of all tested features using a MLP Classification with MLP (Top 3 Features) 100 80 80 Accuracy Rate [%] Accuracy Rate [%] Classification with MLP (All Features) 100 60 40 20 60 40 20 0 0 0 100 200 300 Resolution [dpi] 400 500 0 100 200 300 400 500 Resolution [dpi] Fig.