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A Study of Deep Belief Network Based Chinese Speech Emotion recognition

A Study of Deep Belief Network Based Chinese Speech Emotion ...

引用

International Conference on Computational Intelligence and Security

作者： Bu Chen Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781479974351

This paper presents a deep learning method application to the extraction of emotions included in Chinese speech with a deep belief network (DBN) structure. Eight proper features such as pitch, mel frequency cepstrum coefficient (MFCC) are chosen from Mandarin speech used as network inputs, and a DBN classifier is used instead of traditional shallow learning methods to recognition of emotions. Experiment studies have proven that its recognition rate is higher than that of the traditional back propagation (BP) method and support vector machine (SVM) classifier.

关键词： Speech Feature extraction Speech recognition Emotion recognition Support vector machines Training Speech processing

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Parallelization and Optimization of Molecular Dynamics Simulation on Many Integrated Core

Parallelization and Optimization of Molecular Dynamics Simul...

引用

International Conference on Computational Intelligence and Security

作者： Qian Yin Ruiyi Luo Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Molecular dynamics (MD) simulations are useful in various areas. In this paper, we parallelize and optimize the grid-based MD algorithm on Many Integrated Core (MIC) Architecture. To get full play of the hardware and accelerate computation of MD simulation, we design the parallel structure using multi-threads with OpenMP. Also, various or method such as Array Notification, intrinsic and so on are used to vectorize the application according to the character of MIC for a higher performance. Due that multi-core is also a trendy of CPU and High Performance Computing, our method can be followed by other similar applications and provide a more choice.

关键词： Microwave integrated circuits Instruction sets Force Graphics processing units Algorithm design and analysis Arrays

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Building Extraction Method in Remote Sensing image

Building Extraction Method in Remote Sensing Image

引用

International Conference on Data processing Techniques and Applications for Cyber-Physical Systems, DPTA 2019

作者： Han, Qinzhe Zheng, Xin Yin, Qian Chen, Ziyi Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China College of Information Science and Technology University parkPA United States Beijing Normal University Beijing China

ISBN: (纸本)9789811514678

Identifying buildings in disaster areas quickly and conveniently plays an important role in post-disaster reconstruction and disaster assessment. Aiming at the technical requirements of earthquake relief projects, this paper proposed a building extraction method in remote sensing images by combining traditional digital image processing methods and convolution neural network. First, In the pretreatment process, remote sensing images are transformed from RGB space to HSV space. Then, shadows in the images are weakened by using the Retinex model de-shadowing algorithm. Finally, buildings in the image are classified and recognized by using a convolution neural network. Experiments prove that compared with the fast R-CNN algorithm, the detection accuracy is improved. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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ECoG Analysis with Affinity Propagation Algorithm

ECoG Analysis with Affinity Propagation Algorithm

引用

International Conference on Natural Computation (ICNC)

作者： Yuan Yuan An-bang Xu Ping Guo Jia-cai Zhang Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Analyzing notor imagery electrocardiogram (ECoG) signal is very challenging for it is hard to set up a classifier based on the labeled ECoG obtained in the first session and apply it to the unlabeled test data obtained in the second session. Here we propose a new approach to analyze ECoG trails in the case of session-to-session transfer exists. In our approach, firstly, dimension reduction is performed with independent component analysis (ICA) decomposition. Secondly, ECoG trials are clustered by an unsupervised learning algorithm called affinity propagation. Primary experimental results show that the proposed approach gives the reasonable result than that using the classical K-means clustering algorithm.

关键词： Algorithm design and analysis Independent component analysis Clustering algorithms Feature extraction Electroencephalography pattern analysis Signal analysis image analysis pattern recognition Signal processing

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Dual-cache Structure Based Large Scale Texture Mapping for Real-time Terrain Rendering

Dual-cache Structure Based Large Scale Texture Mapping for R...

引用

IEEE Conference on Robotics, Automation and Mechatronics

作者： Dong Tian Xiaodong Wang Xin Zheng Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

There are two key problems in efficient large scale texture mapping for terrain rendering-efficient data organization and real time data updating in memory. In order to solve these problems, in this paper we propose a quadtree based indexing method to organize multi-resolution images and to fast retrieve data from disk; For memory updating, we present a real time dual-cache structure based updating method, which effectively reduces the frequency of data refresh. We also innovatively use a wavelet image enhancement algorithm to enhance original terrain texture, which obtain richer edge information and give us a more realistic effect in terrain rendering. Through the analysis of storage efficiency and rendering speed of our experiment, this dual-cache structure based method solves rendering speed and memory limit problems perfectly.

关键词： Large-scale systems Terrain mapping Rendering (computer graphics) Indexing Information retrieval image processing pattern recognition Frequency image enhancement image retrieval

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Combining the contrast information with LPQ for texture classification

Combining the contrast information with LPQ for texture clas...

引用

International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

作者： Hussain Dawood Hassan Dawood Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Texture classification is an important problem in image analysis. A considerable amount of research work has been done for local or global rotation invariant feature extraction for texture classification. Local invariant features contain the spatial information, but usually do not have the contrast information. A new hybrid approach is proposed which considers the contrast information in spatial domain and the phase information in frequency domain of the image. It uses the joint histogram of the two complementary features, local phase quantization (LPQ) and the contrast of the image. Support vector machine is used for classification. The experimental results on standard benchmark datasets for texture classification Brodatz and KTH-TIPS2-a show that the proposed method can achieve significant improvement compared to the LPQ, Gabor filer or local Binary pattern methods.

关键词： Histograms Quantization (signal) Support vector machines Transforms Feature extraction Standards Gabor filters

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Complete Two-Dimensional PCA for Face recognition

Complete Two-Dimensional PCA for Face Recognition

引用

International Conference on pattern recognition

作者： Anbang Xu Xin Jin Yugang Jiang Ping Guo Image Processing & Pattern Recognition Laboratory Beijing Normal University Beijing China

We propose a novel method, the complete two-dimensional principal component analysis (complete 2DPCA), for image features extraction. Compared to the original 2DPCA, complete 2DPCA not only gain a higher recognition rate, but also reduce the feature coefficients needed for face recognition. Complete 2DPCA is based on 2D image matrices. Two image covariance matrices are constructed directly using the original image matrix and theirs eigenvectors are derived for image feature extraction. Our experiments were performed on ORL face database, and experimental results show that the proposed method has an encouraging performance

关键词： Principal component analysis Face recognition Covariance matrix Independent component analysis Kernel Feature extraction pattern recognition Lighting image databases Scattering

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Fast Multiple Scattering in Participating Media with Beamlet Decomposition

Fast Multiple Scattering in Participating Media with Beamlet...

引用

International Conference on Computational Intelligence and Security

作者： Yuanlong Wang Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

We propose a fast algorithm which is based on the beam let decomposition for real-time rendering of scenes in participating media with multiple scattering. Firstly, the light source radiation is considered as composed by all particles in the media and each particle radiation is decomposed along different forward directions using the plane decomposition method. Then the multiple scattering radiation of one particle is calculated by the decomposition radiations from its adjacent particles and the light source. Finally, according to the multiple scattering radiation value of each particle, the radiation of the ray which is from viewpoint is calculated using ray marching method, which can be implemented on the graphics processing unit (GPU), and rendering process is highly parallel. The experimental results show that the algorithm can achieve real-time rendering efficiency and enhance the practicality of multiple scattering.

关键词： Scattering Media Rendering (computer graphics) Light sources Absorption Algorithm design and analysis Graphics processing units

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Scene recognition via Combining Information of Neighbors

Scene Recognition via Combining Information of Neighbors

引用

International Conference on Computational Intelligence and Security

作者： Minguang Song Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. When combining PACT with Local difference Magnitude Binary pattern (LMBP), a new representation called Local Difference Binary pattern (LDBP) was proposed and performed better. LDBP is based on the comparisons between center pixel and its neighboring pixels. However, the relationship among neighbor pixels is not considered. In this paper we proposed Local Neighbor Binary pattern (LNBP) to utilize the relationship among neighboring pixels. LNBP provides complementary information regarding neighboring pixels for LDBP. We propose to combine LDBP with LNBP, and used a spatial representation for scene recognition. Experiments on two widely used dataset demonstrate the proposed method can improve the performance of recognition.

关键词： Histograms pattern recognition Transforms Computer vision Principal component analysis Conferences image recognition

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Comparison of linear dimensionality reduction methods in image annotation

Comparison of linear dimensionality reduction methods in ima...

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Shiqiang Li Hussain Dawood Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Dimension reduction methods are often used to analyzing high dimensional data, linear dimension methods are commonly used due to their simple geometric interpretations and for effective computational cost. Dimension reduction plays an important role for feature selection. In this paper, we have given a detailed comparison of state-of-the-art linear dimension reduction methods like principal component analysis (PCA), random projections (RP), and locality preserving projections (LPP). We have determined which dimension reduction method performs better under the FastTag image annotation framework. Experiments are conducted on three standard bench mark image datasets such as CorelSk, IAPRTC-12 and ESP game to compare the efficiency, effectiveness and also memory usage. A detailed comparison among the aforementioned dimension reduction method is given.

关键词： Principal component analysis Benchmark testing Kernel Random access memory Customer relationship management

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