Download Computational Forensics: Second International Workshop, IWCF by Sargur N. Srihari, Katrin Franke PDF

By Sargur N. Srihari, Katrin Franke

This booklet constitutes the refereed lawsuits of the second one foreign Workshop, IWCF 2008, held in Washington, DC, united states, August 2008.

The 19 revised complete papers provided have been rigorously reviewed and chosen from 39 submissions. The papers are geared up in topical sections on developments and demanding situations; scanner, printer, and prints; human id; shoeprints; linguistics;decision making and seek; speech research; signatures and handwriting.

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Additional resources for Computational Forensics: Second International Workshop, IWCF 2008, Washington, DC, USA, August 7-8, 2008. Proceedings

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6). e. area diff and edge rough, while especially the edge dist feature is performing best at 400dpi. Surprisingly, using all features in combination yields a lower performance than using only the edge dist on its own refering to the “ugly duckling” theorem. Testing results: The classification using all features shows a strong influence of the edge rough feature exhibiting only little variance at 100dpi. Since this feature is not among the top 3 pca features, the accuracy for this resolution drops down a lot (Fig.

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.

Therefore, all documents were scanned at 100dpi, 200dpi, 300dpi, 400dpi and stored in the TIFF dataformat to avoid further information loss. To perform classification on character level at least recognition and extraction of the connected components from the scanned document is necessary. After binarization of the scanned image data using Otsu’s method[21], a regional growing algorithm as proposed in [22] was applied for detection. Subsequently, the minimal bounding box rectangle of each detected component was calculated and its content extracted.

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