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RPC-based Adjustment Model for COSMO-SkyMed Stereo Slant/Ground-Range Images

RPC-based Adjustment Model for COSMO-SkyMed Stereo Slant/Gro...

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The 2010 ieee international conference on progress in informatics and computing

作者： Zhen Li School of Remote Sensing and Information Engineering Wuhan University Wuhan,China Guo Zhang,Hong-bo Pan,Qiang Qiang State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing(LIESMARS) Wuhan University Wuhan,China

The use of stereoscopic SAR images offers an alternative to conventional stereo-photogrammetric survey for the generation of Digital Elevation Models(DEMs).Often the SAR ground-range form is more popular with the commercial users,since the pixel spacing on the ground is roughly the same for the different look-angle *** different mathematical descriptions between the slant-range and ground-range products thus make the stereo modeling and adjustment a challenging objective to deal *** work applied sensor model adjustment of range and timing parameters to SAR spotlight range images,promising 3-D mapping accuracies in the range of 2 ***,this adopted the direct least squares method, which is too sensitive in the case of the ground-range images, making it less than optimal for SAR range images' stereo *** this paper,an image based transformation (geometric correction) using a small number of control points(CPs) in cooperation with the Rational Polynomial Coefficient(RPC) model to improve the space intersection accuracy is *** development of such an RPC-based adjustment method is first described,which is very practical to implement and can be applied to SAR slant- or ground-range *** situating several well-distributed trihedral corner reflectors(CRs) within sites and imaging these sites using COSMO-SkyMed's stripmap(SM) mode,the modeling quality of the delivered slant- or ground-range products was validated, and the 3-D mapping potential was also assessed.

关键词： RPC model adjustment SAR range stereo pairs

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Fine-grained Vehicle Recognition Using Hierarchical Fine-Tuning Strategy for Urban Surveillance Videos

Fine-grained Vehicle Recognition Using Hierarchical Fine-Tun...

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2016 ieee international conference on progress in informatics and computing

作者： Qiang Zhang Li Zhuo Xiaochen Hu Jing Zhang Signal & Information Processing Laboratory Beijing University of Technology Collaborative Innovation Center of Electric Vehicles in Beijing

The Fine-grained Vehicle recognition is easily affected by small visual changes. The existing recognition methods have less robustness to these conditions(such as illumination, weather changes, etc.) and the accuracy of vehicle recognition in complex environments cannot achieve a satisfying result. In this paper, a high-accuracy fine-grained vehicle recognition method using Convolutional Neural Network for urban surveillance videos is proposed. Differing from the conventional CNN based methods, which usually obtain a single classification model, the proposed method combines Pre-training and hierarchical fine-tuning to obtain different classification model that can adapt to the change of illumination conditions. In the recognition phase, the input image is evaluated firstly using the proposed image quality assessment method. Then, the classification model is selected adaptively according to the results of quality evaluation and used for recognition. To test our method, we use a publicly vehicle recognition dataset for Urban Surveillance Videos provide by Yang. Experimental results show that the accuracy of the proposed method is up to 98.89%, which is 0.5% higher than Yang's method.

关键词： fine-grained vehicle recognition hierarchical fine-tuning quality assessment

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Pathological Brain Detection Based on Online Sequential Extreme Learning Machine

Pathological Brain Detection Based on Online Sequential Extr...

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2016 ieee international conference on progress in informatics and computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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High speed scientific data transfers using software defined networking 2

High speed scientific data transfers using software defined ...

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2nd Workshop on Innovating the Network for Data-Intensive Science, INDIS 2015

作者： Newman, Harvey Legrand, Iosif Mughal, Azher Voicu, Ramiro Kcira, Dorian Bunn, Julian Marcos, Jorge Lopez, Luis Barczyk, Artur Bredel, Michael Cury, Samir Lapadatescu, Vlad Spiroulu, Maria Vlimant, Jean-Roch Hendricks, Wayne Tam, Thomas Martelli, Edoardo Foster, David Papadopoulis, Christos Shannigrahi, Susmit Hoeft, Bruno Lee, Andy Lee, Gene Tierney, Brian Pouyoul, Eric Monga, Inder Guok, Chin Bell, Greg Johnston, Bill Colling, David Rand, Duncan Fayer, Simon Boyd, Eric DeMar, Phil Wu, Wenji Spentzouris, Panagiotis Ibarra, Julio Alvarez, Heidi Bezerra, Jeronimo Keen, Andrew Yeh, Edmund Liu, Ran Chen, James Mambretti, Joe Timoteo, Sergio Salamao, Jorge Cox, Chip Wildish, Tony Moura, Alex Dias, Gustavo Ciuffo, Leandro Stanton, Michael Von Malenstein, Gerben Rothenberg, Christian Esteve Arantes, Dalton Soares Melo, Darli Barbosa, Felipe Rudge Novaes, Sergio Iope, Rogerio Leal, Beraldo Bach, Eduardo Costa, Marcio Pavani, Gustavo Batista, Jorge Ball, Robert Hockett, Roy McKee, Shawn De, Kaushik Gable, Ian Sobie, Randal Tackett, Alan Melo, Andrew Sheldon, Paul Caltech 1200 E California Blvd. PasadenaCA91125 United States Brazil United States CANARIE Canada CERN Switzerland Colorado State University United States Germany Echostreams Germany ESnet Spain Imperial College London United Kingdom Internet2 United States Fermilab United States Florida International University/AmLight United States Michigan State University United States Northeastern University United States Northwestern University United States Padtec United States Princeton University United States Brazil SurfNET Brazil Brazil Brazil Universidade Federal Do ABC Brazil United States United States Canada Vanderbilt University United States

ISBN: (纸本)9781450340021

The massive data volumes acquired, simulated, processed and analyzed by globally distributed scientific collaborations continue to grow exponentially. One leading example is the LHC program, now at the start of its second three year data taking cycle, searching for new particles and interactions in a previously inaccessible range of energies, which has experienced a 70% growth in peak data transfer rates over the last 12 months alone. Other major science programs such as LSST and SKA, and other disciplines ranging from earth observation to genomics, are expected to have similar or great needs than the LHC program within the next decade. The development of new methods for fast, efficient and reliable data transfers over national and global distances, and a new generation of intelligent, software-driven networks capable of supporting multiple science programs with diverse needs for high volume and/or real-time data delivery, are essential if these programs are to continue to progress, and meet their goals. In this paper we describe activities of the Caltech High Energy Physics team and collaborators, related to the use Software Defined Networking to help achieve fast and efficient data distribution and access. Results from Supercomputing 2014 are presented together with our work on the Advanced Network Services for the Experiments project, and a new project developing a Next Generation Integrated SDN Architecture, as well as our plans for Supercomputing 2015. © 2015 ACM.

关键词： Software defined networking

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Face Image Illumination Quality Assessment for Surveillance Video Using KPLSR

Face Image Illumination Quality Assessment for Surveillance ...

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2016 ieee international conference on progress in informatics and computing

作者： Xiaochen Hu Li Zhuo Jing Zhang Xiaoguang Li Signal & Information Processing Laboratory Beijing University of Technology Collaborative Innovation Center of Electric Vehicles in Beijing

Illumination condition is one of the most important factors that affect the face recognition performance. Face image illumination quality assessment can predict the face recognition performance under various illumination conditions, which will improve the accuracy and efficiency of the face recognition system. However, the quality scores calculated by the existing methods are weakly correlated with the performance of face recognition. Face images with high scores often present bad recognition performance, while face images with low scores unexpectedly present good recognition performance. To predict the recognition performance more accurately, a kernel partial least squares regression(KPLSR) based face image illumination quality assessment method for surveillance video is proposed in this paper. The mapping relationship between illumination conditions and face recognition performance is modeled using KPLSR. Taken the fact that different regions of face image have different importance on face recognition, the features of luminance and contrast of sub-blocks are extracted to reflect the illumination conditions. Matching score of the face recognition system is calculated to measure the recognition performance. Experimental results show that, compared with the existing methods, the quality scores calculated by the proposed method are strong correlated with the recognition performance. The proposed method can also meet the requirements of real time processing.

关键词： face image illumination quality kernel partial least squares regression surveillance video real time

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Vehicle Classification for Traffic Surveillance Videos Based on Spatial Location Information and Sparse Representation-based Classifier

Vehicle Classification for Traffic Surveillance Videos Based...

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2016 ieee international conference on progress in informatics and computing

作者： Liying Jiang Li Zhuo Haixia Long Xiaochen Hu Jing Zhang Signal & Information Processing Laboratory Beijing University of Technology Collaborative Innovation Center of Electric Vehicles in Beijing

Vehicle classification plays an important part in Intelligent Transport System(ITS). However, the existing vehicle classification methods are not very robust to various changes such as lighting, weathers, noises, and the classification accuracy has been requiring to be improved. Sparse Representation-based Classifier(SRC) is not sensitive to the shortage and damage of data, the feature selection methods, is robust to lighting changes of the images, and can achieve excellent performance in multiclass classification problems. Therefore, this paper presents a new method based on spatial location information and SRC. Firstly, taken the specific characteristics of vehicles into consideration, the features of HOG(Histogram of Oriented Gradient) and HU moments are extracted to characterize the property of vehicles. In addition, the spatial location information is added to HU moment features in this paper to improve its ability to distinguish and describe vehicles. Then, vehicles are classified into six classes(large bus, car, motorcycle, minibus, truck and van) by SRC. The performance evaluation is carried out on the dataset which consists of 13300 vehicle images extracted from the real highway surveillance videos. The experimental results show that, using the proposed method, the time cost of classification can be speeded up by about 1.57 times and the average classification precision can achieve 96.53%, which is drastically improved by more than 2.7% compared to other existing methods.

关键词： Vehicle classification HOG feature HU moments Spatial location information SRC

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Mass Data Storage Platform for Smart Grid

Mass Data Storage Platform for Smart Grid

引用

2016 ieee international conference on progress in informatics and computing

作者： Huahui Lyu Ping Li Yuning Xiao Hongjie Qian Bin Sheng Ruimin Shen Information Department China Southern Power Grid Department of Mathematics and Information Technology The Education University of Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences

With the continuous development of power systems, the scale of power grid is increasingly expanding, and the structure of it is becoming more and more complex. Traditional electrical network management cannot meet the current requirements. In this paper, we use load forecasting as a starting point, carrying on the exploration regarding heterogeneous multi-source data of the smart grid and doing research on shortterm load forecasting. In the process of building the application platform of smart grid, we propose some solutions to solve these problems. In the study of heterogeneous multi-source data fusion, we proposed a fusion method of grid data based on CIM/XML after comparing the current method and do numerical simulation on our application platform of smart grid to test its feasibility. At the same time, we also do a lot research on the current situation of load forecasting. After analyzing the advantage and disadvantage of some normal forecasting methods, for example, the peer-fold ratio method, the ration smoothing method, grey prediction and variation coefficient method, we developed two comprehensive approaches, the method of weighted mean and the ratio method based on grey prediction, which will increase the accuracy, general applicability and stability of load forecasting. Through these studies and efforts, we build a friendly and visually interactive application platform, which will lay the foundation of further development about the application platform of smart grid.

关键词： smart grid load forecasting data fusion CIM XML

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Intelligent decision making and Consensus

Intelligent decision making and Consensus

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2017 international conference on progress in informatics and computing(PIC)

作者： Enrique Herrera-Viedma Department of Computer Science and Artificial Intelligence University of Granada

Societal and technological trends make the decision environments more and more *** emergence of the intelligent decision making technology provides a good decision support to deal with these complex decision making problems, and Artificial Intelligence tools play a core role in the intelligent decision making technology. Nowadays we find many real world problems in which intelligent decision making processes are developed by automatic decision making systems that support individual or organizational decision making processes supported by different information technologies as the Web, Social Networks, Internet of the Things, Big Data, and Artificial Intelligence tools(as Computational Intelligence tools). In many cases, we find that the intelligent decision making processes involve the use of both preference modelling and consensus processes. The preference modelling deals with the representation and models of the preferences provided by the experts in the problems. The fuzzy logic is a computational intelligence tool that provides an adequate framework to deal with the uncertainty presented in the user opinions. The fuzzy preference modelling has been satisfactory applied in intelligent decision making. On the other hand, consensus is an important area of research in intelligent decision making. Consensus is defined as a state of mutual agreement among members of a group where all opinions have been heard and addressed to the satisfaction of the group. A consensus reaching process is a dynamic and iterative process composed by several rounds where the experts express, discuss and modify their preferences. In this talk we show the main elements of the decision making activity in presence of vague information and uncertainty. In particular, we present different concepts to understand the research developed in the area of fuzzy decision making as fuzzy preference relations to represent expert preferences, aggregation operators, selection processes of alternativ

关键词： Intelligent decision making and Consensus

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Pixel-based Image Fusion Using Wavelet Transform for SPOT and ETM+ Image

Pixel-based Image Fusion Using Wavelet Transform for SPOT an...

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The 2010 ieee international conference on progress in informatics and computing

作者： Hongbo Wu Center for Forest Operations and Environment Northeast Forestry University Harbin,P.R.China Yanqiu Xing Center for Forest Operations and Environment Northeast Forestry University Harbin,P.R.China

Image fusion means merging two or more images in such a way as to preserving the most desirable characteristics of each *** of standard image fusion methods are often successful at adding spatial detail into the multispectral imagery but distort the colour information in the fusion ***,the paper presents an approach for multi-resolution image fusion of a high-resolution SPOT(Satellite Pour l'Observation de la Terre, SPOT) panchromatic image and a low-resolution Landsat 7 ETM+ multispectral image based on wavelet transform(WT) combined with filtering in the Fourier ***,the images were decomposed to some wavelet coefficients by Mallat algorithm;Secondly,the wavelet transform methods add the wavelet coefficients of the SPOT PAN image to ETM+ image. Finally,the fused image is reconstructed by performing the inverse wavelet transform for obtaining multispectral images of higher spatial *** order to evaluate the quality of the fused images,three quantitive indicators such as Gradients, RMSE,Correlation Coefficients were defined in the *** the mean time,the results from a number of wavelet-based image fusion schemes which are the intensity-hue-saturation(IHS) and high-pass filter method(HPF) are compared and these fusion methods were used to synthesize the Landsat ETM+ data and SPOT-5 PAN data *** evaluation results showed that the WT fusion methods perform the fusion of SPOT PAN image and ETM+ image better than IHS and HPF,specially in preserving both spectral and spatial *** results showed that the proposed WT fusion algorithm works well in multiresolution fusion and also preserve the original color or spectral characteristics of the input image data.

关键词： wavelet transform spot assessment image fusion pixel

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Analysis of Changes in Agronomic Parameters and Disease Index of Rapeseed Leaf Leukoplakia Based on Spectra

Analysis of Changes in Agronomic Parameters and Disease Inde...

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The 9th international conference on Computer and computing Technologies in Agriculture (CCTA 2015)

作者： Kunya Fu Hongxin Cao Wenyu Zhang Weixin Zhang Daokuo Ge Yan Liu Chunhuan Feng Weitao Chen College of Agronomy Nanjing Agricultural University Institute of Agricultural Economy and Information/Engineering Research Center for Digital Agriculture Jiangsu Academy of Agricultural Sciences

ISBN: (纸本)9783319483566

The non-destructive, rapid and accurate monitoring diagnosis of rapeseed diseases is of significance for sustainable development of rapeseed production and environment protection. The spectrum data of rapeseed leaf leukoplakia were collected in the experimental Farm of Jiangsu Academy of Agricultural Science in 2013 and 2014. Firstly, the common distinctive bands of the disease and the health were found by comparing re flectance spectrum of leaves in the field and under black background. The results showed that with the progress of the growth period, re flectance of disease leaves decreased earlier than healthy leaves. It was the best period to identify rapeseed leukoplakia from11 days after early anthesis to 9 days after finish flowering to identify rapeseed leukoplakia in the field due to during this period the re flectance of healthy leaves remained at 35 % while the disease diseased to 30 %. The sensitive band was in the range of 760–1080 nm. The correlation among disease index(DI), agronomic parameters, and the re flectance of the disease samples were analysed, and the results showed that there were high correlations between DI, and agronomic parameters and re flectance, e.g., the correlation between the leaf moisture content and the re flectance in 460 nm, 550 nm, 650 nm, 710 nm, 760 nm,1480 nm, and 1600 nm, between the leaf nitrogen content and the re flectance in810 nm, 870 nm, 1080 nm, 1280 nm, 1320 nm, 1540 nm, 1600 nm, 1650 nm,and 1700 nm, and between the SPAD value and the re flectance in 1200 nm,1280 nm, and 1540 nm had significance with p < 0.01. The quantitative models of agronomic parameters based on reflectance were developed by stepwise regression, principal component analysis, and curve fitting. The data of rapeseed leukoplakia in 2013 and rapeseed virus in 2014 were used to test. The results showed that in the same disease test, the quantitative models of moisture content based on reflectance were fit well. In the different disease test, the quan

关键词： Rapeseed leukoplakia Spectrum reflectance Disease index Agronomic parameters Reflectance Newton meter rapeseed Leukoplakia moisture content maladjustment foliage Spectra leaf nitrogen content

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