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The difference between sample distributions of public data sets and specific scenes can be very significant. As a result, the deployment of generic human detectors in real-world scenes most often leads to sub-optimal detection performance. To avoid the labor-intensive task of manual annotations, we propose a semi-supervised approach for training deep convolutional networks on partially labeled data...
Generating photo-realistic images from multiple style sketches is one of challenging tasks in image synthesis with important applications such as facial composite for suspects. While machine learning techniques have been applied for solving this problem, the requirement of collecting sketch and face photo image pairs would limit the use of the learned model for rendering sketches of different styles...
The convolutional neural network (CNN) is more and more popular in computer vision and widely used in acoustic signal processing, image classification, and image segmentation. In this work, an architecture which is a combination of the 3-D convolutional neural network and the long short term memory (LSTM) was proposed for action recognition. It stacks the consecutive video frames, extracts spatial...
In this paper, a system identification method based on BP-network is proposed to realize the study of dynamic characteristics of high power LED luminaire. LED input-output characteristic relations are obtained according to the lighting system structure, and response curves are received using the method of input isolation. A Back Propagation network model is established to simulate input and output...
To handle problems created by large data sets, we propose a method that uses a decision tree to decompose a given data space and trains SVMs on the decomposed regions. Although there are other means of decomposing a data space, we show that the decision tree has several merits for large-scale SVM training. First, it can classify some data points by its own means, thereby reducing the cost of SVM training...
It is of great significance and application value to train acupuncture learners with modern information technology. We synthetically utilize the technologies of Computer Graphics and Virtual Reality to propose a data optimization method of 3D human body model and make visualization processing of the human body model as well as pick up the human body acupuncture points by using JOGL (Java OpenGL)....
We experimentally investigated transmission performance of 16 QAM-OFDM signal over 25-km fiber and 5-m air distance using 60 GHz ROF technology. It is the first time that the details of optimal equalization technique for 60 GHz OFDM-ROF systems are presented.
Aiming at the complexity of interior and variety of exterior structure of stock price system, this paper analyzes principles of stock prediction based on BP neural network, provides prediction model for stock market by utilizing three-layered feed forward neural networks, presents topology of network, principles of determining the number of hidden layers, selection and pretreatment of sample data...
The purpose of this paper is based on radial basis function neural network (RBFN) to develop a self-constructing least Wilcoxon-generalized RBFN fuzzy inference system (LW-GRBFNFIS) and applied to nonlinear function approximation and chaotic time series prediction. As is well known in statistics, the resulting linear function by using the rank-based least Wilcoxon (LW) norm approximate to linear function...
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