A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tes...A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.展开更多
The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling c...The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.展开更多
基金This project is supported by National Natural Science Foundation of China(No.50075079).
文摘A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.
文摘The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.
