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Recognition of human actions by using wearable sensors has become an important research field. Segmentation to sensor data is a vital issue in reconstructing and understanding human daily actions, and strongly affects the accuracy of human actions recognition. Traditional online segmentation approaches are mostly designed for one-dimensional sensor data, which greatly limits these approaches to multi-dimensional...
When the gait feature is recognized, it will be hard to meet the real-time need due to the complexity and long calculated time of the recognition algorithm. Under this circumstance, a novel sparse representation based human gait recognition method is put forth which is based on. First of all, human silhouette is established and gait period is calculated. Next, we adopt Shifting Energy Image (SEI)...
This paper introduces a method for the efficient comparison and retrieval of near duplicates of a query video from a video database. The method generates video signatures from histograms of orientations of optical flow of feature points computed from uniformly sampled video frames concatenated over time to produce time series, which are then aligned and matched. Major incline matching, a data reduction...
This paper outlines several multimedia systems that utilize a multimodal approach. These systems include audiovisual based emotion recognition, image and video retrieval, and face and head tracking. Data collected from diverse sources/sensors are employed to improve the accuracy of correctly detecting, classifying, identifying, and tracking of a desired object or target. It is shown that the integration...
A new blind source extraction method in widespread noise conditions is proposed, which is based on multiple frequency-domain independent component analysis (FDICA) combining projection back and spectral subtraction. In addition, We implement the proposed method to digital signal processor (DSP) for a more realistic real-time operation, and develop a new blind source extraction (BSE) microphone which...
Increasingly large text damsels and the high dimensionality associated with natural language create a great challenge in text mining, In this research, a systematic study is conducted. in which three different document representation methods for text are used, together with three Dimension Reduction Techniques (DRT), in the context of the text clustering problem. Several standard benchmark datasets...
In this work, a method that combines wavelet transform and Bayesian network is developed for the classification of the auditory brainstem response (ABR). First the wavelet transform is applied to extract the important features of the ABR by thresholding and matching the wavelet coefficients. A Bayesian network is then built up based on several variables obtained from these significant wavelet coefficients...
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