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An Effective Method for Modeling Two-dimensional Sky Background of LAMOST

Proceedings of the International Astronomical Union 2017年第S325期12卷 63-66页

作者： Hasitieer Haerken Fuqing Duan Jiannan Zhang Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University 100875 Beijing China email: fqduan@bnu.edu.cnpguo@*** National Astronomical Observatories & Chinese Academy of Sciences 100012 Beijing China email: hastear@***jnzhang@***

Each CCD of LAMOST accommodates 250 spectra, while about 40 are used to observe sky background during real observations. How to estimate the unknown sky background information hidden in the observed 210 celestial spectra by using the known 40 sky spectra is the problem we solve. In order to model the sky background, usually a pre-observation is performed with all fibers observing sky background. We use the observed 250 skylight spectra as training data, where those observed by the 40 fibers are considered as a base vector set. The Locality-constrained Linear Coding (LLC) technique is utilized to represent the skylight spectra observed by the 210 fibers with the base vector set. We also segment each spectrum into small parts, and establish the local sky background model for each part. Experimental results validate the proposed method, and show the local model is better than the global model.

关键词： LAMOST celestial spectra sky background modeling

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Study of point spread function of astronomical object imaging

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International Conference on Information Science and Applications, ICISA 2016

作者： Yu, Jian Yin, Qian Guo, Ping College of Computer and Information Engineering Hanshan Normal University ChaozhouGuangdong521041 China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China

ISBN: (纸本)9789811005565

The research of point spread function (PSF) of astronomical object imaging is very important to the astronomical image restoration. In this paper, the simulated atmospheric turbulent phase screen, the short exposure PSF and longexposure PSF during the astronomical object imaging are studied. One novel Gaussian process move-and-superposition model for representing the final astronomical object long exposure PSF formation is proposed. The proposed model obtains more accurate final PSF of astronomical object imaging. © Springer Science+Business Media Singapore 2016.

关键词： Optical transfer function

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image Stitching with single-hidden layer feedforward Neural Networks

Image Stitching with single-hidden layer feedforward Neural ...

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International Joint Conference on Neural Networks

作者： Min Yan Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)9781509006212

In this paper, a novel image stitching method is proposed, which utilizes scale-invariant feature transform (SIFT) feature and single-hidden layer feedforward neural network (SLFN) to get higher precision of parameter estimation. In this method, features are extracted from the image sets by the SIFT descriptor and form into the input vector of the SLFN. The output of the SLFN is those translation, rotation and scaling parameters with respect to reference and registered image sets. We also apply a fast learning scheme, called pseudoinverse learning, to train SLFN to get higher training efficiency. Comparative experiments are performed between our proposed method and the traditional random sample consensus (RANSAC) based method. The results show that our method has the advantage not only at accuracy but also remarkably at fast speed.

关键词： SIFT descriptor Feedforward neural network image registration image stitching feedforward neural nets image stitching image registration Parameter estimation fast speed

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Kernel Selection with Evolutionary Algorithm for Multiple Kernel Independent Component Analysis

Kernel Selection with Evolutionary Algorithm for Multiple Ke...

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International Joint Conference on Neural Networks

作者： Peng Wu Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)9781509006212

Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model is a more difficult problem if one has not sufficient experience. To deal with this problem, an evolution based method to select the kernel model of KICA is proposed in this paper. There are two main features of the proposed method: one is that using a multiple kernel model, a convex combination of several single kernels, replaces the single kernel model;another is that particle swarm optimization (PSO) algorithm is utilized to find the combination weights of the composite kernel. Experiments conducted on separating one-dimensional mixed signals, nature images, and spectroscopic CCD images showed that using multiple kernels model with PSO kernel selection algorithm can enhance the performance of KICA.

关键词： Seeds Kernel evolutionary algorithm Independent component analysis

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image Representation via Sub-dictionary based Sparse Coding

Image Representation via Sub-dictionary based Sparse Coding

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International Joint Conference on Neural Networks

作者： Bingxin Xu Qian Yin Ping Guo Hongzhe Liu Beijing Key Laboratory of Information Service Engineering Beijing Union University Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)9781509006212

In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The proposed sparse coding method involves a series of sub-dictionaries. Each sub-dictionary contains all the training samples except for those from one particular category. For the test sample to be represented, all the sub-dictionaries should linearly represent it apart from the one that does not contain samples from that label, and this sub-dictionary is called irrelevant sub-dictionary. This new regularization item restricts the sparsity of each sub-dictionary's residual, and this restriction is helpful for classification. The experimental results demonstrate that the proposed method is superior to the previous related sparse representation based classification.

关键词： image representation Sparse coding sparse representation-based classification Linearly

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Pulsar Candidate Selection by Assembling Positive Sample Emphasized Classifiers

Pulsar Candidate Selection by Assembling Positive Sample Emp...

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International Conference on Computational Intelligence and Security

作者： Yao Yao Xin Xin Ping Guo School of Computer Science and Technology Beijing Institute of Technology Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781509048410

Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions of observations efficiently. Considering the number of positive training samples are relatively too small and the cost of incorrectly labeling a real pulsar candidate as negative is large, we propose a novel hierarchical candidate-sifting model by emphasizing the cost of incorrect prediction of positive samples and assembling multiple classifiers trained with different weighting parameters. Experiments on three pulsar selection datasets demonstrate our proposed method improves the pulsar-sifting performance a lot according to several standard evaluation metrics.

关键词： Training Support vector machines Predictive models Feature extraction Measurement Labeling Histograms

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Correction to: Internet gaming disorder: deficits in functional and structural connectivity in the ventral tegmental area-Accumbens pathway

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Brain imaging and behavior 2019年第4期13卷 1182页

作者： Ruonan Wang Min Li Meng Zhao Dahua Yu Yu Hu Corinde E Wiers Gene-Jack Wang Nora D Volkow Kai Yuan School of Life Science and Technology Xidian University Xi'an Shaanxi 710071 People's Republic of China. Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xi'an People's Republic of China. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia 014010 People's Republic of China. National Institute on Alcoholism and Alcohol Abuse National Institutes of Health Bethesda MD 20892 USA. National Institute on Drug Abuse National Institutes of Health Bethesda MD 20892 USA. School of Life Science and Technology Xidian University Xi'an Shaanxi 710071 People's Republic of China. kyuan@***. Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Xi'an People's Republic of China. kyuan@***. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia 014010 People's Republic of China. kyuan@***. National Institute on Alcoholism and Alcohol Abuse National Institutes of Health Bethesda MD 20892 USA. kyuan@***. Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin People's Republic of China. kyuan@***.

The original version of this article contained mistakes, and the authors would like to correct them. The correct details are given below.

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A Pseudoinverse Incremental Algorithm for Fast Training Deep Neural Networks with Application to Spectra pattern recognition

A Pseudoinverse Incremental Algorithm for Fast Training Deep...

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International Joint Conference on Neural Networks

作者： Ke Wang Ping Guo Qian Yin A-Li Luo Xin Xin School of Computer Science and Technology Beijing Institute of Technology Image Processing and Pattern Recognition Laboratory Beijing Normal University Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences

ISBN: (纸本)9781509006212

Deep learning scheme has received significant attention during these years, particularly as a way of building hierarchical representations from unlabeled data for a variety of signal and information processing tasks. However, deep neural networks suffer from slow learning speed since most used training algorithms are based on variations of the gradient descent algorithms which require iterative optimization and thus are time-consuming. In addition, a series of control parameters need to be specified empirically which lacks of the theoretical guidance, and current learning algorithms for deep networks are not very suitable to incremental learning scenario. To address these issues, we propose a fast learning scheme in this paper. The basic idea of our approach is to pre-train basic units such as auto-encoders of the deep architecture in an analytical way without any iterative optimization procedure. This scheme is also extended to an incremental learning version. The experimental result shows the superiority of our approach over the state-of-the-art gradient descent based algorithms. To demonstrate the impact of our algorithm on complicated real world applications, we give an example of its performance in astronomical spectra pattern recognition.

关键词： astronomical spectra Spectra pattern recognition learning (artificial intelligence) recognition (Psychology) Neural network algorithms control parameter Optimization Procedure descent Fasting Functional training Road

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Removal of random-valued impulse noise by Khalimsky grid

Removal of random-valued impulse noise by Khalimsky grid

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Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

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

In this paper, based on Khalimsky grid, a new Random-valued Impulse noise identification and removal method is proposed. Khalimsky grid can presents the neighborhood relationship among the pixels in the sliding window, effectively. The local statistics of Khalimsky grid is used to define an adaptive threshold range to identify the central pixel in current sliding window as noisy or noise free in an iterative way. The identified noisy pixel is replaced by local statistics of propose vertical direction based noise removal method. The performance of the propose method is evaluated on different test images and compared with state-of-the-art methods. Experimental results show that the propose method can identify the impulse noise, as well as can preserve the detailed information of an image, efficiently.

关键词： Noise Noise measurement Tin Multimedia communication Filtering algorithms Asia Digital multimedia broadcasting

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Comparison of linear dimensionality reduction methods in image annotation

Comparison of linear dimensionality reduction methods in ima...

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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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