Temat: principal component analysis PCA - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Biometric watermarking for security enhancement in digital images
Autorzy:: Wójtowicz, W.
Powiązania:: https://bibliotekanauki.pl/articles/115437.pdf
Data publikacji:: 2013
Wydawca:: Fundacja na Rzecz Młodych Naukowców
Tematy:: images security
watermarking technique
discrete wavelet transform (DWT)
biometrics
principal component analysis (PCA)
Opis:: In this paper some preliminary investigation on combination of watermarking technique with biometric data to increase security of digital images in case of medical images is proposed. Performance of watermarking algorithm, based on discrete wavelet transform (DWT) decomposition, that incorporates biometric watermark is elaborated. The frequency domain were chosen as it is proven, that this domain provides better robustness against attacks and leads to less perceptibility of an embedded watermark. To assure confidentiality of patient data their hand geometry features are embedded instead of patient’s name. Proposed system is evaluated by measuring the similarity between embedded and extracted biometric codes.
Źródło:: Challenges of Modern Technology; 2013, 4, 4; 7-11
2082-2863
2353-4419
Pojawia się w:: Challenges of Modern Technology
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Feature vector or time-series – comparison of gestures representations in automatic gesture recognition systems
Autorzy:: Barczewska, K.
Wójtowicz, W.
Moszkowski, T.
Powiązania:: https://bibliotekanauki.pl/articles/115720.pdf
Data publikacji:: 2015
Wydawca:: Fundacja na Rzecz Młodych Naukowców
Tematy:: principal component analysis (PCA)
independent component analysis (ICA)
neural networks
sign language
automatic recognition
analiza składowych głównych (PCA)
analiza składowych niezależnych (ICA)
sieci neuronowe
język migowy
automatyczne rozpoznawanie
Opis:: In this paper, we performed recognition of isolated sign language gestures - obtained from Australian Sign Language Database (AUSLAN) – using statistics to reduce dimensionality and neural networks to recognize patterns. We designated a set of 70 signal features to represent each gesture as a feature vector instead of a time series, used principal component analysis (PCA) and independent component analysis (ICA) to reduce dimensionality and indicate the features most relevant for gesture detection. To classify the vectors a feedforward neural network was used. The resulting accuracy of detection ranged between 61 to 87%.
Źródło:: Challenges of Modern Technology; 2015, 6, 1; 1-5
2082-2863
2353-4419
Pojawia się w:: Challenges of Modern Technology
Dostawca treści:: Biblioteka Nauki

Artykuł

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