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In this paper, a hybrid visual feature extraction method that combines the extended locally linear embedding (LLE) with visemic linear discriminant analysis (LDA) was presented for the audio-visual speech recognition (AVSR). Firstly the extended LLE is presented to reduce the dimension of the mouth images, which constrains the scope of finding mouth data neighborhood to the corresponding individual's...
In this paper an approach for personal biometric identification is presented based on extraction of ECG features and classification with RBFNN. We perform denoising and segmentation on the input signal, after which we realize dimensionality reduction and feature extraction based on PCA transform. The separability of the selected features is improved by applying LDA. The final stage of the proposed...
Since EEG is one of the most important sources of information in diagnosis of epilepsy, several researchers tried to address the issue of decision support for such a data. We present a method for classifying epilepsy of full spectrum EEG recordings. In the proposed method, autoregressive (AR) model is used to acquire power spectrum of EEG signals, then dimension of the extracted feature vectors is...
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