To improve the recognition accuracy of off-line handwritten Tibetan characters the local gradient direction histograms based on the wavelet transform are proposed as the recognition features.First for a Tibetan charac...To improve the recognition accuracy of off-line handwritten Tibetan characters the local gradient direction histograms based on the wavelet transform are proposed as the recognition features.First for a Tibetan character sample image the first level approximation component of the Haar wavelet transform is calculated.Secondly the approximation component is partitioned into several equal-sized zones. Finally the gradient direction histograms of each zone are calculated and the local direction histograms of the approximation component are considered as the features of the character sample image.The proposed method is tested on the recently developed off-line Tibetan handwritten character sample database.The experimental results demonstrate the effectiveness and efficiency of the proposed feature extraction method.Furthermore compared with the detail components the approximation component contributes more to the recognition accuracy.展开更多
A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification...A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification of non-stationary echo data from objects with different property.The feature extraction is derived from the Discrete Dyadic Wavlet Transform (DDWT) of the echo through the undecimated algorithm. The motivation we use the DDWT is that it is time-shift-invariant which is beneficial for localization of edge, and the wavelet coefficients at larger scale represent the main shape feature of echo, i.e. edge, and the noise and modulated high-frequency components are reduced with scale increased. Some experimental results using real data which contain 144 samples from 4 classes of lake bottoms with different sediments are provided. The results show that our approach is a prospective way to represent wideband echo for reliable recognition of nonstationary echo with great variability.展开更多
This paper introduced a novel, simple and ef-fective method to extract the general feature of two surface EMG (electromyography) signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) s...This paper introduced a novel, simple and ef-fective method to extract the general feature of two surface EMG (electromyography) signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) surface EMG signal. After surface EMG (SEMG) signal was decomposed to the fourth resolution level with wavelet packet transform (WPT), its whole scaling space (with frequencies in the interval (0Hz, 500Hz]) was divided into16 frequency bands (FB). Then wavelet coefficient entropy (WCE) of every FB was calculated and corre-spondingly marked with WCE(n) (from the nth FB, n=1,2,…16). Lastly, some WCE(n) were chosen to form WCE feature vector, which was used to distinguish FS surface EMG signals from FP surface EMG signals. The result showed that the WCE feather vector consisted of WCE(7) (187.25Hz, 218.75Hz) and WCE(8) (218.75Hz, 250Hz) can more effectively recog-nize FS and FP patterns than other WCE feature vector or the WPT feature vector which was gained by the combination of WPT and principal components analysis.展开更多
This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet pa...This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.展开更多
In this paper, a novel method based on dual-tree complex wavelet transform(DT-CWT) and rotation invariant local binary pattern(LBP) for facial expression recognition is proposed. The quarter sample shift (Q-shift) DT-...In this paper, a novel method based on dual-tree complex wavelet transform(DT-CWT) and rotation invariant local binary pattern(LBP) for facial expression recognition is proposed. The quarter sample shift (Q-shift) DT-CWT can provide a group delay of 1/4 of a sample period, and satisfy the usual 2-band filter bank constraints of no aliasing and perfect reconstruction. To resolve illumination variation in expression verification, low-frequency coefficients produced by DT-CWT are set zeroes, high-frequency coefficients are used for reconstructing the image, and basic LBP histogram is mapped on the reconstructed image by means of histogram specification. LBP is capable of encoding texture and shape information of the preprocessed images. The histogram graphs built from multi-scale rotation invariant LBPs are combined to serve as feature for further recognition. Template matching is adopted to classify facial expressions for its simplicity. The experimental results show that the proposed approach has good performance in efficiency and accuracy.展开更多
With the continuous development of the computer, people's requirements for computers are also getting more and more, so the brain-computer interface system (BCI) has become an essential part of computer research. ...With the continuous development of the computer, people's requirements for computers are also getting more and more, so the brain-computer interface system (BCI) has become an essential part of computer research. Emotion recognition is an important task for the computer to understand social status in BCI. Affective computing (AC) aims to develop the model of emotions and advance the affective intelligence of computers. There are various emotion recognition approaches. The method based on electroencephalogram (EEG) is more reliable because it is higher in accuracy and more objective in evaluation than other external appearance clues such as emotion expression and gesture. In this paper, we use the wavelet transform (WT) to extract three kinds of EEG features in time, and frequency domain, which are sub-band energy, energy ratio and root mean square of wavelet coefficients. They reflect the emotion related to EEG activities well. The average classification accuracy of support vector machine (SVM) can reach 82.87%, which indicates that these three features are very effective in emotion recognition. On the other hand, compared with international affective picture system (IAPs), EEG data collected by Chinese affective picture system (CAPs) stimulation has a higher emotion recognition rate, indicating that there are cultural background differences in emotions.展开更多
In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung so...In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung sounds(sounds with wheezes or rales). The proposed method includes two main steps: Firstly, the wavelet packet transform(WPT) is used to extract the original features of lung sounds; then the genetic algorithm(GA) is used to select the best feature set. The obtained optimal feature set is sent to four different classifiers to evaluate the performance of the proposed method. Experimental results show that the feature set obtained by the proposed method provides a higher classification accuracy of 94.6% in comparison with the best wavelet packet basis approach and multi-scale principal component analysis(PCA) approach. Meanwhile, the proposed method has effective generalization performance and can obtain the best feature set without priori knowledge of lung sounds.展开更多
基金The National Natural Science Foundation of China(No.60963016)the National Social Science Foundation of China(No.17BXW037)
文摘To improve the recognition accuracy of off-line handwritten Tibetan characters the local gradient direction histograms based on the wavelet transform are proposed as the recognition features.First for a Tibetan character sample image the first level approximation component of the Haar wavelet transform is calculated.Secondly the approximation component is partitioned into several equal-sized zones. Finally the gradient direction histograms of each zone are calculated and the local direction histograms of the approximation component are considered as the features of the character sample image.The proposed method is tested on the recently developed off-line Tibetan handwritten character sample database.The experimental results demonstrate the effectiveness and efficiency of the proposed feature extraction method.Furthermore compared with the detail components the approximation component contributes more to the recognition accuracy.
文摘A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification of non-stationary echo data from objects with different property.The feature extraction is derived from the Discrete Dyadic Wavlet Transform (DDWT) of the echo through the undecimated algorithm. The motivation we use the DDWT is that it is time-shift-invariant which is beneficial for localization of edge, and the wavelet coefficients at larger scale represent the main shape feature of echo, i.e. edge, and the noise and modulated high-frequency components are reduced with scale increased. Some experimental results using real data which contain 144 samples from 4 classes of lake bottoms with different sediments are provided. The results show that our approach is a prospective way to represent wideband echo for reliable recognition of nonstationary echo with great variability.
文摘This paper introduced a novel, simple and ef-fective method to extract the general feature of two surface EMG (electromyography) signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) surface EMG signal. After surface EMG (SEMG) signal was decomposed to the fourth resolution level with wavelet packet transform (WPT), its whole scaling space (with frequencies in the interval (0Hz, 500Hz]) was divided into16 frequency bands (FB). Then wavelet coefficient entropy (WCE) of every FB was calculated and corre-spondingly marked with WCE(n) (from the nth FB, n=1,2,…16). Lastly, some WCE(n) were chosen to form WCE feature vector, which was used to distinguish FS surface EMG signals from FP surface EMG signals. The result showed that the WCE feather vector consisted of WCE(7) (187.25Hz, 218.75Hz) and WCE(8) (218.75Hz, 250Hz) can more effectively recog-nize FS and FP patterns than other WCE feature vector or the WPT feature vector which was gained by the combination of WPT and principal components analysis.
基金Supported by the National Basic Research Program("973"Program, No2005CB724303 )
文摘This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.
文摘In this paper, a novel method based on dual-tree complex wavelet transform(DT-CWT) and rotation invariant local binary pattern(LBP) for facial expression recognition is proposed. The quarter sample shift (Q-shift) DT-CWT can provide a group delay of 1/4 of a sample period, and satisfy the usual 2-band filter bank constraints of no aliasing and perfect reconstruction. To resolve illumination variation in expression verification, low-frequency coefficients produced by DT-CWT are set zeroes, high-frequency coefficients are used for reconstructing the image, and basic LBP histogram is mapped on the reconstructed image by means of histogram specification. LBP is capable of encoding texture and shape information of the preprocessed images. The histogram graphs built from multi-scale rotation invariant LBPs are combined to serve as feature for further recognition. Template matching is adopted to classify facial expressions for its simplicity. The experimental results show that the proposed approach has good performance in efficiency and accuracy.
基金supported by the National Nature Science Foundation of China(No.61503423,H.P.Jiang)。
文摘With the continuous development of the computer, people's requirements for computers are also getting more and more, so the brain-computer interface system (BCI) has become an essential part of computer research. Emotion recognition is an important task for the computer to understand social status in BCI. Affective computing (AC) aims to develop the model of emotions and advance the affective intelligence of computers. There are various emotion recognition approaches. The method based on electroencephalogram (EEG) is more reliable because it is higher in accuracy and more objective in evaluation than other external appearance clues such as emotion expression and gesture. In this paper, we use the wavelet transform (WT) to extract three kinds of EEG features in time, and frequency domain, which are sub-band energy, energy ratio and root mean square of wavelet coefficients. They reflect the emotion related to EEG activities well. The average classification accuracy of support vector machine (SVM) can reach 82.87%, which indicates that these three features are very effective in emotion recognition. On the other hand, compared with international affective picture system (IAPs), EEG data collected by Chinese affective picture system (CAPs) stimulation has a higher emotion recognition rate, indicating that there are cultural background differences in emotions.
基金Funded by the International Science and Technology Cooperation Foundation of Chongqing Science and Technology Commission(Grant No.cstc2012gg-gjhz0023)the 2013 Innovative Team Construction Project of Chongqing Universities
文摘In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung sounds(sounds with wheezes or rales). The proposed method includes two main steps: Firstly, the wavelet packet transform(WPT) is used to extract the original features of lung sounds; then the genetic algorithm(GA) is used to select the best feature set. The obtained optimal feature set is sent to four different classifiers to evaluate the performance of the proposed method. Experimental results show that the feature set obtained by the proposed method provides a higher classification accuracy of 94.6% in comparison with the best wavelet packet basis approach and multi-scale principal component analysis(PCA) approach. Meanwhile, the proposed method has effective generalization performance and can obtain the best feature set without priori knowledge of lung sounds.
