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Impervious surface is a significant indicator of urban environments. In order to better understand the pattern of urban expansion, this study adopted random forest algorithm for a long-term urban impervious surface mapping of Wuhan in China. We produced 23-year (1993 to 2015) urban impervious surface mapping data via a dense Landsat time series and the results indicated that the impervious surface...
Tree models for human pose estimation have been prevailed in the last decade, which are effective in human pose estimation. This paper aims to incorporate the appearance symmetry of human limb parts into tree model and address the problem of the wrong detection of human limbs. For a pair of symmetrical limbs, such as for legs and arms, their appearances are similar that can use a distance to represent...
With the rapid development of healthcare industry, the overwhelming amounts of electronic health records (EHRs) have been well documented and shared by healthcare institutions and practitioners. It is important to take advantage of EHR data to develop an effective disease risk management model that not only predicts the progression of the disease, but also provides a candidate list of informative...
With the ultimate intent of improving the quality of life, identification of human's affective states on the collected electroencephalogram (EEG) has attracted lots of attention recently. In this domain, the existing methods usually use only a few labeled samples to classify affective states consisting of over thousands of features. Therefore, important information may not be well utilized and performance...
Using hashing algorithms to learn binary codes representation of data for fast approximate nearest neighbor (ANN) search has attracted more and more attentions. Most existing hashing methods employ various hash functions to encode data. The resulting binary codes can be obtained by concatenating bits produced by those hash functions. These methods usually have two main steps: projection and thresholding...
This study using computer image processing and artificial neural network sensor technologies constructs a method of identifying ice slurry density based on the value of ice color image. The method is applied to the Jinan section of the Yellow River through the ice image acquisition, R/G color extraction, network learning and training, the final output target value of ice or water, and the actual image...
An improved artificial fish swarm algorithm called ASFSA is proposed. It could facilitate the selection of values for Step and Visual to meet the balance of algorithm speed and effect. A new SVM parameters selection method based on the ASFSA is described, and the kernel parameter γ and regularization parameter C can both be optimized well. The application case shows that the performance of SVM with...
The brassiere-wearing effect is produced by the combined action of body shape and brassiere. The process is so complicated that it is an issue for people to realize what functions the different factors perform in the change of bust appearance. In this research, an artificial neural network model was applied, in which the input neurons contained 11 possible factors including body measurements and brassiere...
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed...
In the development of advanced vehicle safety systems, monitoring the driverpsilas vigilance level and issuing an alert when he is not paying enough attention to the road is a promising way to reduce the road accidents. In such driver monitoring systems, developing a reliable real-time driver eye detection method is a crucial part. In this paper, a rear-time eye detection method using support vector...
In this paper, a novel framework is proposed for classifying images, which integrates several sets of support vector machines(SVM) on multiple low level image features. In the proposed framework several global image features are extracted from the input images, and SVM using linear kernel with probability outputs are constructed on each feature. The outputs of the SVM classifiers are then combined...
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