Národní úložiště šedé literatury Nalezeno 1 záznamů.  Hledání trvalo 0.01 vteřin. 
Reconstruction and Enhancement of Damaged Parts of Fingerprint Images
Špila, Andrej ; Rydlo, Štěpán (oponent) ; Heidari, Mona (vedoucí práce)
This thesis deals with the problem of fingerprint image reconstruction with focus on non- recoverable regions affected by various skin diseases. A generative adversarial network with learnable convolutional gabor filter layer was trained on preprocessed dataset of real fingerprint images. The work demonstrates that the trained model can reliably repair small corrupted regions of arbitrary shapes and in case of larger holes, the global quality score of reconstructed fingerprints evaluated by MINDTCT module from NIST biometric image software is increased compared to original fingerprint. A standardized format for fingerprint images that helped stabilize the results when training generative models is proposed.

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