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Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use low-level features and 2) lack high-level context. In this paper, we propose a novel deep learning based algorithm that can tackle both these problems...
This paper presents an automated method for seizure detection in EEGs using an increment entropy (IncrEn) and support vector machines (SVMs). The IncrEn is a measure of the complexity of time series, which characterizes both the permutation of values and the temporal order of values. The IncrEn is used to extract features of epileptic EEGs and normal EEGs. The SVMs are employed to classify seizure...
This paper utilizes the deep learning algorithm to classify the Street View images. We did some research to find the appropriate convolutional neural network model that suits the classification of the street view images. We firstly collected our own dataset. Based on the convolutional neural network model AlexNet and according to the characteristics the dataset mentioned above to adjust the model...
Voice conversion is a technique that aims to transform the individuality of source speech so as to mimic that of target speech while keeping the message unaltered, where the Gaussian mixture model based methods are most commonly used. However, these methods suffer from over-smoothing and over-fitting problems. In our previous work, we proposed to use Gaussian processes to alleviate over-fitting. Despite...
Discriminative locality alignment (DLA) has been successfully applied in similar handwritten Chinese character recognition (SHCCR). But, the performance of DLA heavily depends on the choice of parameters and the optimal parameters among different groups of similar characters are not consistent. To address this problem, we present an improved method with few parameters, called adaptive discriminative...
In this paper, a new abnormal activity detection algorithm is proposed for multi-camera surveillance applications. The proposed algorithm models the entire scene covered by the multi-camera system as a network. In this network, each node corresponds to a segmentation of the entire scene and each edge represents the activity correlation between the corresponding segmentations. Based on this network,...
In this paper, we present a new voice conversion method based on the state-space model (SSM). A modified version of the conventional SSM model is first proposed to describe the relationship between the source speech and the target speech in the spectral domain. Then the expectation maximum (EM) and variational Bayesian (VB) algorithms are individually employed to estimate the SSM parameters, resulting...
One of the most recent models for voice conversion is the classical LPC analysis-synthesis model combined with GMM, which aims to separate information from excitation and vocal tract and to learn the transformation rules with statistical methods. However, it does not work well as it is supposed to be due to the inaccuracy of the extracted feature information as well as the overly-smoothed spectral...
The data of welding seam width and height were obtained by TIG welding experiments. Two models were established using BP neural networks: One can predict the weld seam dimensions by inputting the welding parameters, and the other model can perform oppositely. Originally by inputting given welding seam dimensions to the model one, the welding parameters can be predicted. Then change the parameters...
This paper presents a novel voice morphing system which reproduces high quality speech while maintaining the majority of the target characteristics. Bi-GMM is named for using GMM technique to estimate mapping functions as well as a codebook generated by GMM either. Compared with the traditional GMM technique, a maximum likelihood estimation framework combined with codebook compensation technique is...
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