检索结果-内蒙古大学图书馆

Diffusion tensor based curvature-driven difiusion model for digital inpainting

Journal of Computational Information Systems 2015年第1期11卷 185-194页

作者： Qu, Lei Huang, Linsheng Wei, Sui Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

In this paper, we formulate the digital inpainting as a curvature-driven anisotropic concentration difiusing process. The difiusion strength along the gradient directions is defined as a function of curvature, so that the "Connectivity Principle" can be satisfied. Difierent to the classic Curvature-Driven Difiusion (CDD) model, the difiusion along the edge directions can be easily integrated into the difiusion equation in our model by using difiusion tensor. As result, more exibility in designing the local difiusion behavior is allowed, and the CDD model can be generalized as a special case. We show that our model can boost inpainting speed 3fi6 times and at the same time has similar inpainting quality compared to the widely used CDD model. Experimental results on synthetic and real images have verified the stability and eficiency of our model. ©, 2014, Journal of Computational Information Systems. All right reserved.

关键词： Tensors

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Fusion of Polarization Image Using Bidimensional Empirical Mode Decomposition

Fusion of Polarization Image Using Bidimensional Empirical M...

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International Conference on Computer Science and Mechanical Automation

作者： Dexiang Zhang Zihong Chen Jiaxing Li Jingjing Zhang Key Lab. of Intelligent Computing and Signal Processing Anhui University The School of Electrical Engineering and Automation Anhui University

ISBN: (纸本)9781467391672

Empirical mode decomposition (EMD) provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Bidimensional empirical mode decomposition (BEMD) techniques decompose an image into several bidimensional intrinsic mode functions (BIMFs) and a bidimensional residue (BR). Firstly, several polarization images can be decomposed into several BIMFs with multi-scales using BEMD. For the BIMF coefficients, the teager energy-based method is used. For the each BIMF coefficients, the area-based teager energy larger value of information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused image. Experimental results show that the proposed algorithm gives more satisfactory results than the traditional image fusion algorithms in preserving the edges and texture information.

关键词： Bidimensional empirical mode decomposition Image decomposition Polarization image Image fusion

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Hierarchical Covering Algorithm

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Tsinghua Science and Technology 2014年第1期19卷 76-81页

作者： Jie Chen Shu Zhao Yanping Zhang Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University

The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.

关键词： deep architecture hierarchy fuzzy equivalence relation covering tree MNIST dataset

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Speech Endpoint Detection Based on EMD and Higher Order Statistics in Noisy Environments

Speech Endpoint Detection Based on EMD and Higher Order Stat...

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2015 5th International Conference on Information Engineering for Mechanics and Materials(ICIMM 2015)

作者： Dexiang Zhang Jiaxing Li Zihong Chen Key Lab.of Intelligent Computing and Signal Processing Anhui University The School of Electrical Engineering and Automation of Anhui University

Accurate endpoint detection is crucial for speech recognition accuracy. This paper presents a new technique for speech endpoint detection in a noisy environment based on the empirical mode decomposition(EMD) algorithm and higher order statistics. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions(IMFs), which is a zero-mean AM-FM component. Then higher order statistics of the IMF components can be used to extract the desired feature for endpoint detection. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experimental results show that the performance of the proposed algorithm is noticeable in the real speech signal tests with different SNR.

关键词： Endpoint Detection EMD Higher Order Statistics Noisy Environments

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A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks 40

A novel approach for automatic acoustic novelty detection us...

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40th IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2015

作者： Marchi, Erik Vesperini, Fabio Eyben, Florian Squartini, Stefano Schuller, Bjorn Machine Intelligence and Signal Processing Group Technische Universität München Germany A3LAB Department of Information Engineering Universit Politecnica Delle Marche Italy Department of Complex and Intelligent Systems University of Passau Germany Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9781467369978

Acoustic novelty detection aims at identifying abnormal/novel acoustic signals which differ from the reference/normal data that the system was trained with. In this paper we present a novel unsupervised approach based on a denoising autoencoder. In our approach auditory spectral features are processed by a denoising autoencoder with bidirectional Long Short-Term Memory recurrent neural networks. We use the reconstruction error between the input and the output of the autoencoder as activation signal to detect novel events. The autoencoder is trained on a public database which contains recordings of typical in-home situations such as talking, watching television, playing and eating. The evaluation was performed on more than 260 different abnormal events. We compare results with state-of-theart methods and we conclude that our novel approach significantly outperforms existing methods by achieving up to 93.4% F-Measure. © 2015 IEEE.

关键词： Acoustic Novelty Detection Bidirectional LSTM Denoising Autoencorder Recurrent Neural Networks

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Multiple-Instance Learning with Instance Selection via Constructive Covering Algorithm

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Tsinghua Science and Technology 2014年第3期19卷 285-292页

作者： Yanping Zhang Heng Zhang Huazhen Wei Jie Tang Shu Zhao the Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University the Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University1Hefei 230601 China the Department of Computer Science and Technology Tsinghua University

Multiple-Instance Learning （MIL） is used to predict the unlab.led bags＇ lab.l by learning the lab.led positive training bags and negative training *** bag is made up of several unlab.led instances.A bag is lab.led positive if at least one of its instances is positive,otherwise *** multiple-instance learning methods with instance selection ignore the representative degree of the selected *** example,if an instance has many similar instances with the same lab.l around it,the instance should be more representative than *** on this idea,in this paper,a multiple-instance learning with instance selection via constructive covering algorithm （MilCa） is *** MilCa,we firstly use maximal Hausdorff to select some initial positive instances from positive bags,then use a Constructive Covering Algorithm （CCA） to restructure the structure of the original instances of negative *** an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training ***,a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted *** results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms.

关键词： multiple-instance learning instance selection constructive covering algorithm maximal Hausdorff

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An algorithm for reading activity recognition based on electrooculogram

An algorithm for reading activity recognition based on elect...

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International Conference on Information, Communications and signal processing

作者： Rui OuYang Zhao Lv XiaoPei Wu Anhui University Hefei Anhui CN Ministry of Education Key Lab of Intelligent Computing & Signal Processing Co-Innovation Center for Information Supply & Assurance Technology Anhui University Hefei China

ISBN: (纸本)9781467372190

The research of the relationship between eye-movement and human activity has become a hot spot in the field of the human-computer interaction. In order to recognize the eye-movement patterns related to reading tasks, an algorithm based on electrooculogram(EOG) has been proposed in this paper. In the proposed approach, wavelet packet decomposition was firstly used to smooth EOG signals during reading tasks, and then, according to the predefined coding strategy, the smoothed EOG waveform is translated into a coding string to represent current reading state. Finally, Levenshtein distance is employed to measure the similarity of the template string and the output string. The experiments under the experimental paradigms of autonomy-reading and stimulus-reading and reading with rest state have been carried out. The mean accurate has reached 90%. The experimental results indicate that the proposed algorithm can effectively recognize human reading activity state.

关键词： Electrooculography Wavelet packets Encoding Algorithm design and analysis Electrodes

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Social sensing enhanced time estimation for bus service

Social sensing enhanced time estimation for bus service

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作者： Liu, Jin Li, Juan Cui, Xiaohui Niu, Xiaoguang Sun, Xiaoping Zhou, Jing State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China International School of Software Wuhan University Wuhan China Computer School Wuhan University Wuhan China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science Communication University of China China

The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEAs and road condition attributions PRCAs. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEAs of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. © 2015 John Wiley & Sons, Ltd.

关键词： Buses

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Speech enhancement based on EMD and wavelet threshold in noisy environments

Speech enhancement based on EMD and wavelet threshold in noi...

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4th International Conference on Materials Science and Information Technology, MSIT 2014

作者： Chen, Zi Qin Zhang, De Xiang Yuan, Da Ling Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China The School of Electrical Engineering and Automation Anhui University Hefei 230601 China

ISBN: (纸本)9783038351733

Speech enhancement is crucial for speech recognition accuracy. How to eliminate the effect of the noise constitutes a challenging problem in speech processing. This paper presents a new technique for speech enhancement in a noisy environment based on the empirical mode decomposition (EMD) algorithm and wavelet threshold. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then wavelet threshold of the IMF components can be used to eliminate the effect of the noise for speech enhancement. Experimental results show that the proposed speech enhancement by de-noising algorithm is possible to achieve an excellent balance between suppresses noise effectively and preserves as many target characteristics of original signal as possible. © (2014) Trans Tech Publications, Switzerland.

关键词： Empirical mode decomposition

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Fusion of visual and infrared image based on NSCT

Fusion of visual and infrared image based on NSCT

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作者： Chen, Zi Hong Zhang, De Xiang Yan, Qing Zhang, Jing Jing Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China School of Electrical Engineering and Automation of Anhui University Hefei 230601 China

ISBN: (纸本)9783038351382

Non-subsampled contourlet transform (NSCT) is the combination of the multi-scale analysis and multi-directional analysis in processing high-dimensional signals and has better approximation precision and better sparse description. A novel and efficient fusion method of infrared image and visible image based on NSCT is proposed. Firstly, source fusion images can be decomposed into low-frequency coefficients and high-frequency coefficients using the NSCT. For the low-frequency coefficients, the average fusion algorithm is used. For the each directional high frequency sub-band coefficients, the larger value and region average gradient maximum criterion is used to select the better coefficients for fusion. Experimental results show that the proposed algorithm can achieve better result compared with the traditional image fusion algorithms. © (2014) Trans Tech Publications, Switzerland.

关键词： Image fusion

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