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ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications

作者： Dongdong, Cao Qian, Yin Ping, Guo IEEE Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)0769525288

The fusion of multi-source remote sensing data is to offer improved accuracies in land cover classification. The conventional fusion methods such as HIS and PCA can not enhance information and simultaneously preserve high fidelity. Thus, the fused image is not preferable for classification. In this paper, the multi-source remote sensing data fusion based on Mallat algorithm for classification is proposed. The purpose of fusion is to create a new image that is more suitable for recognition. The topic focuses on the pyramid decomposition and choosing coefficients in the fusion process. The performance of proposed method is assessed by statistical methods and its effectiveness also testified by classification accuracies. © 2006 IEEE.

关键词： Remote sensing

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Pulsar candidate selection using ensemble networks for FAST drift-scan survey

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Science China(Physics,Mechanics & Astronomy) 2019年第5期62卷 61-70页

作者： HongFeng Wang WeiWei Zhu Ping Guo Di Li SiBo Feng Qian Yin ChenChen Miao ZhenZhao Tao ZhiChen Pan Pei Wang Xin Zheng XiaoDan Deng ZhiJie Liu XiaoYao Xie XuHong Yu ShanPing You Hui Zhang FAST Collaboration Image Processing and Pattern Recognition Laboratory College of Information Science and TechnologyBeijing Normal UniversityBeijing 100875China CAS Key Laboratory of FAST Chinese Academy of SciencesBeijing 100101China School of Information Management Dezhou UniversityDezhou 253023China Image Processing and Pattern Recognition Laboratory School of Systems ScienceBeijing Normal UniversityBeijing 100875China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China 不详

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope(FAST) Survey(CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system(PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks(CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual-GPU and 24-core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches.

关键词： pulsars neural networks data analysis

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Machine learning techniques and chi-square feature selection for cancer classification using SAGE gene expression profiles

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PAKDD 2006 Workshop on Data Mining for Biomedical Applications, BioDM 2006

作者： Jin, Xin Xu, Anbang Bie, Rongrang Guo, Ping College of Information Science and Technology Beijing Normal University Beijing 100815 China Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)3540331042

Recently developed Serial Analysis of Gene Expression (SAGE) technology enables us to simultaneously quantify the expression levels of tens of thousands of genes in a population of cells. SAGE is better than Microarray in that SAGE can monitor both known and unknown genes while Microarray can only measure known genes. SAGE gene expression profiling based cancer classification is a better choice since cancers may be due to some unknown genes. Whereas a wide range of methods has been applied to traditional Microarray based cancer classification, relatively few studies have been done on SAGE based cancer classification. In our study we evaluate popular machine learning methods (SVM, Naive Bayes, Nearest Neighbor, C4.5 and RIPPER) for classifying cancers based on SAGE data. In order to deal with the high dimensional problem, we propose to use Chi-square for tag/gene selection. Both binary classification and multicategory classification are investigated. The experiments are based on two human SAGE datasets: brain and breast. The results show that SVM and Naive Bayes are the top-performing SAGE classifiers and that Chi-square based gene selection can improve the performance of all the five classifiers investigated. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Biotechnology

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Kernel based weighted group sparse representation classifier

Kernel based weighted group sparse representation classifier

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15th International Conference on Human-Computer Interaction, HCI International 2013

作者： Xu, Bingxin Guo, Ping Chen, C. L. Philip Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Faculty of Science and Technology University of Macau China

ISBN: (纸本)9783642393419

Sparse representation classification (SRC) is a new framework for classification and has been successfully applied to face recognition. However, SRC can not well classify the data when they are in the overlap feature space. In addition, SRC treats different samples equally and ignores the cooperation among samples belong to the same class. In this paper, a kernel based weighted group sparse classifier (KWGSC) is proposed. Kernel trick is not only used for mapping the original feature space into a high dimensional feature space, but also as a measure to select members of each group. The weight reflects the importance degree of training samples in different group. Substantial experiments on benchmark databases have been conducted to investigate the performance of proposed method in image classification. The experimental results demonstrate that the proposed KWGSC approach has a higher classification accuracy than that of SRC and other modified sparse representation classification. © 2013 Springer-Verlag.

关键词： Classification (of information)

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PupilFace: A Cascaded Face Detection and Location Network Fusing Attention 18th

PupilFace: A Cascaded Face Detection and Location Network Fu...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Li, Xiang Zou, Jiancheng Image Processing and Pattern Recognition Laboratory North China University of Technology Beijing100000 China

ISBN: (纸本)9783030893699

Although the development of uncontrolled face detection and location technology have made great progress, there are some problems needing to be solved in more complicated situation, such as massive occlusion and pose variation. In this paper, we propose a robust one-stage face detection and location network named PupilFace. It can locate faces of different sizes at the pixel level in complex scenarios. Specifically, we have made contributions in the following three aspects: (1) Using a lightweight backbone, we can not only detect images of dense faces, but also mark facial landmarks in pictures of various scale. In this paper, the pictures are difficult to detect because of massive occlusion or tiny faces. On the WIDER FACE hard test set, PupilFace performs better than other state-of-the-art networks. (2) The addition of the attention module–Hard Efficient Channel Attention (HECA), proposed by us, enhances the connection between the feature channels and improves the detection performance without reducing the dimension. The parameters and computations of HECA, against the parameters and computations of MobileNetV2 are 9 vs. 3.34M and 5.1e−4 GFLOPs vs. 0.32 GFLOPs. (3) We can employ varying-depths backbones accordingly to different detection and location tasks, so the model can be popularized in different fields. Extra annotations and code have been made available at: https://***/Ideal-maths/PupilFace. © 2021, Springer Nature Switzerland AG.

关键词： Face recognition

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Building Extraction Method in Remote Sensing image

Building Extraction Method in Remote Sensing Image

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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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Comparative studies of feature extraction methods with application to face recognition

Comparative studies of feature extraction methods with appli...

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2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

作者： Jiang, Yunfei Guo, Ping Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)1424409918

In face recognition, the dimensionality of raw data is very high, dimension reduction (Feature Extraction) should be applied before classification. There exist several feature extraction methods, commonly used are Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. In this paper, we present a comparative study of some feature extraction methods for face recognition in the same conditions. The methods evaluated here include Eigenfaces, Kernel Principal Component Analysis (KPCA), Fisherfaces, Direct Linear Discriminant Analysis (D-LDA), Regularized Linear Discriminant Analysis (R-LDA), and Kernel Direct Discriminant Analysis (KDDA). For the purpose of comparison on feature extraction methods, we adopt Nearest Neighbor (NN) algorithm from existed classifiers of face recognition, since this classifier is common and simpleness. Empirical studies are conducted to evaluate these feature extraction methods with images from ORL Face Database, and it is found that in most cases LDA-based methods are efficient than PCA-based ones. © 2007 IEEE.

关键词： Feature extraction

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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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Improved PSO algorithm with harmony search for complicated function optimization problems

Improved PSO algorithm with harmony search for complicated f...

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9th International Symposium on Neural Networks, ISNN 2012

作者： Yu, Jian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China College of Mathematics and Information Technology Hanshan Normal University Chaozhou Guangdong 521041 China

ISBN: (纸本)9783642313455

Improved particle swarm optimization algorithm with harmony search (IHPSO) is proposed in this paper. This algorithm takes particle swarm search direction estimation mechanism and harmony search (HS) approach to particle swarm optimization (PSO) algorithm, which increases the search capability of PSO algorithm considerably. The proposed algorithm initializes a new search with harmony pitch adjusting or random selection when PSO search direction is estimated incorrectly. This can provide further opportunities of finding better solutions for the particle swarm by guiding the entire particle swarm to promising new regions of the search space and accelerating the search. PSO, HPSO and IHPSO, as well as other advanced PSO procedures from the literature were compared on several benchmark test functions extensively. Statistical analyses of the experimental results indicate that the performance of IHPSO is better than the performance of PSO and HPSO. © 2012 Springer-Verlag.

关键词： Particle swarm optimization (PSO)

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Kernel ICA applied to feature extraction for image annotation

Kernel ICA applied to feature extraction for image annotatio...

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2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

作者： Xu, Bingxin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9781424481354

In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel mapping component, is adopted to extract low-level features from the original image data. Then these feature vectors are mapped to high-level semantic words using SVM to annotate images with labels in a given semantic label set. Comparative studies have done for the performance of KICA with traditional color histogram and discrete cosine transform features. The experimental results show that the proposed method is capable of extracting the components of images as key features, and with these features to map into semantic categories, higher accuracy is achieved. © 2010 IEEE.

关键词： Support vector machines

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