Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by link...Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.展开更多
In this article we propose to combine an integrated method, the PCA-GMM method that generates a relatively improved segmentation outcome as compared to conventional GMM with Kalman Filtering (KF). The combined new met...In this article we propose to combine an integrated method, the PCA-GMM method that generates a relatively improved segmentation outcome as compared to conventional GMM with Kalman Filtering (KF). The combined new method the PCA-GMM-KF attempts tracking multiple moving objects;the size and position of the objects along the sequence of their images in dynamic scenes. The obtained experimental results successfully illustrate the tracking of multiple moving objects based on this robust展开更多
This study investigated how background speech affected L1 and L2 reading of Chinese English major students. English, Dutch, and Mandarin Chinese were respectively set as the second language （L2）, foreign language ...This study investigated how background speech affected L1 and L2 reading of Chinese English major students. English, Dutch, and Mandarin Chinese were respectively set as the second language （L2）, foreign language （FL）, and first language （L1） background speech conditions. Self-paced word-by-word reading paradigm was used to collect the response time （RT） of each word. The conventional analysis revealed that L1 background speech exerted the most disruptive effect on both L1 and L2 reading could be phonological and could be at the and suggested that the background speech effect stage of phonological processing of L1 and L2 reading. It also implied that L1 phonological processing could be simultaneously activated during L2 reading. Spectral analysis of ten subjects＇ reading data indicated that pink noise existed in each time series of word RT of L1 and L2 reading in each condition. It provided clear evidence that L1 and L2 reading processing are similar with different concurrent background speech.展开更多
文摘Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.
文摘In this article we propose to combine an integrated method, the PCA-GMM method that generates a relatively improved segmentation outcome as compared to conventional GMM with Kalman Filtering (KF). The combined new method the PCA-GMM-KF attempts tracking multiple moving objects;the size and position of the objects along the sequence of their images in dynamic scenes. The obtained experimental results successfully illustrate the tracking of multiple moving objects based on this robust
文摘This study investigated how background speech affected L1 and L2 reading of Chinese English major students. English, Dutch, and Mandarin Chinese were respectively set as the second language （L2）, foreign language （FL）, and first language （L1） background speech conditions. Self-paced word-by-word reading paradigm was used to collect the response time （RT） of each word. The conventional analysis revealed that L1 background speech exerted the most disruptive effect on both L1 and L2 reading could be phonological and could be at the and suggested that the background speech effect stage of phonological processing of L1 and L2 reading. It also implied that L1 phonological processing could be simultaneously activated during L2 reading. Spectral analysis of ten subjects＇ reading data indicated that pink noise existed in each time series of word RT of L1 and L2 reading in each condition. It provided clear evidence that L1 and L2 reading processing are similar with different concurrent background speech.
