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15th International computer Conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2018

作者： Peng, Jing Liu, Yong-Bo Yima, Y. He, Yong Zheng, Ze-Zhong Zhang, Ya-Meng Li, Jiang Yunnan Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming650217 China School of Resources and Environment University of Electronic Science and Technology of China Chengdu611731 China Sichuan Institute of Geo-Environment Monitoring Chengdu Sichuan610081 China State Key Laboratory of Remote Sensing Science Beijing100101 China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Land and Resources Shenzhen518040 China Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin541004 China State Key Laboratory of Water Resources and Hydropower Engineering Sciences Wuhan University Wuhan430072 China Department of Electrical and Computer Engineering Old Dominion University Norfolk23529 United States

ISBN: (纸本)9781728115351

Power substation is a vital component of power transmission systems. However, their location information, longitude and latitude, maybe incorrect due to human errors. For example, an employee of a power grid company may carelessly input wrong location information to the system database. In practice, these errors residue in database and are difficult to be corrected or even unaware of because human naturally are insensitive to these long numbers. Correct location information is critical and it will ensure destroyed power substations to be quickly pinpointed and repaired after earthquake. In this paper, we develop a deep transfer learning approach to automatically verify power substation location information based upon Google Earth (GE)images. We first extract an image patch for a substation from GE using the recorded location information in database. We then apply the deep transfer learning model to verify whether the image patch contains a power substation. We validated the proposed method on more than 1000 substations in Yunnan Electric Power Grid. Our experimental results showed that the Xception deep transfer model achieved the best accuracy of 98.5% among four compared machine learning methods with an image size of 299×299 pixels. The developed model is a promising tool to verify the location information of power substations in database. © 2018 IEEE.

关键词： Database systems

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A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation

arXiv

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arXiv 2017年

作者： Mitianoudis, Nikolaos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece

—Directional or Circular statistics are pertaining to the analysis and interpretation of directions or rotations. In this work, a novel probability distribution is proposed to model multidimensional sparse directional data. The Generalised Directional Laplacian Distribution (DLD) is a hybrid between the Laplacian distribution and the von Mises-Fisher distribution. The distribution’s parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data. The author explores the application of the derived DLD mixture model to cluster sound sources that exist in an underdetermined instantaneous sound mixture. The proposed model can solve the general K × L (K Copyright © 2017, The Authors. All rights reserved.

关键词： Laplace transforms

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Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder

arXiv

引用

arXiv 2018年

作者： Fan, Yaxiang Wen, Gongjian Li, Deren Qiu, Shaohua Levine, Martin D. National University of Defense Technology Changsha Hunan410073 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan Hubei430071 China Department of Electrical and Computer Engineering Center for Intelligent Machines McGill University 3480 University Street MontrealQCH3A2A7 Canada

We present a novel end-to-end partially supervised deep learning approach for video anomaly detection and localization using only normal samples. The insight that motivates this study is that the normal samples can be associated with at least one Gaussian component of a Gaussian Mixture Model (GMM), while anomalies either do not belong to any Gaussian component. The method is based on Gaussian Mixture Variational Autoencoder, which can learn feature representations of the normal samples as a Gaussian Mixture Model trained using deep learning. A Fully Convolutional Network (FCN) that does not contain a fully-connected layer is employed for the encoder-decoder structure to preserve relative spatial coordinates between the input image and the output feature map. Based on the joint probabilities of each of the Gaussian mixture components, we introduce a sample energy based method to score the anomaly of image test patches. A two-stream network framework is employed to combine the appearance and motion anomalies, using RGB frames for the former and dynamic flow images, for the latter. We test our approach on two popular benchmarks (UCSD Dataset and Avenue Dataset). The experimental results verify the superiority of our method compared to the state of the arts. Index Terms-Anomaly detection, video surveillance, variational autoencoder, Gaussian mixture model, dynamic flow, two-stream network. Copyright © 2018, The Authors. All rights reserved.

关键词： Gaussian distribution

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Building Change Detection Improvement Using Topographic Correction Models

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Advances in remote sensing 2017年第1期6卷 1-22页

作者： Shabnam Jabari Yun Zhang CRC Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick Fredericton Canada Institution of Remote Sensing and Geographic Information System Peking University Beijing China

In the change detection application of remote sensing, commonly the variation in the brightness values of the pixels/objects in bi-temporal image is used as an indicator for detecting changes. However, there exist effects, other than a change in the objects that can cause variations in the brightness values. One of the effects is the illumination difference on steep surfaces mainly steeproofs of houses in very high resolution images, specifically in off-nadir images. This can introduce the problem of false change detection results. This problem becomes more serious in images with different view-angles. In this study, we propose a methodology to improve the building change detection accuracy using imagery taken under different illumination conditions and different view-angles. This is done by using the Patch-Wise Co-Registration (PWCR) method to overcome the misregistration problem caused by view-angle difference and applying Topographic Correction (TC) methods on pixel intensities to attenuate the effect of illumination angle variation on the building roofs. To select a proper TC method, four of the most widely used correction methods, namely C-correction, Minnaert, Enhanced Minnaert (for slope), and Cosine Correction are evaluated in this study. The results proved that the proposed methodology is capable to improve the change detection accuracy. Specifically, the correction using the C-correction and Enhanced Minnaert improved the change detection accuracy by around 35% in an area with a large number of steep-roof houses imaged under various solar angles.

关键词： Topographic Correction Off-Nadir imagery Building Change Detection Patch-Wise Co-Registration (PWCR)

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SEGMENTATION OF FLUORESCENCE MICROSCOPY imageS USING THREE DIMENSIONAL ACTIVE CONTOURS WITH INHOMOGENEITY CORRECTION

SEGMENTATION OF FLUORESCENCE MICROSCOPY IMAGES USING THREE D...

引用

International Symposium on Biomedical Imaging

作者： Soonam Lee Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University Department of Electrical and Computer Engineering Indiana University Division of Nephrology School of Medicine Indiana University

ISBN: (纸本)9781509011735

image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy based on the use of three dimensional (3D) active contours with inhomogeneity correction. The correction information utilizes 3D volume information while addressing intensity inhomogeneity across vertical and horizontal directions. Experimental results demonstrate that the proposed method achieves better performance than other reported methods.

关键词： image segmentation fluorescence microscopy multiphoton microscopy

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LAND PRICE PREDICTION BASED ON RANDOM FOREST

LAND PRICE PREDICTION BASED ON RANDOM FOREST

引用

IEEE International Geoscience and remote sensing Symposium

作者： Ankai Hou Pingchuan Zhong Zezhong Zheng Mingcang Zhu Yong He Qiuying Li Biao Zhang Fang Huang Guoqing Zhou Jiang Li State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing PRC Land and Resources Department of Sichuan Province Chengdu Sichuan PRC Sichuan Institute of Geo-Environment Monitoring Chengdu Sichuan PRC Center for Information and Geoscience University of Electronic Science and Technology of China Chengdu Sichuan PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin Guangxi PRC Departrnent of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

Now, the urbanization process of China is accelerating. Urban land price is of great interest for the government to make reasonable policies and keep the healthy development of land market. Based on the data source of dynamic monitoring system and the statistical yearbook of Chengdu city, we identifies the related factors influencing the comprehensive land price of Chengdu. Firstly, we identified the nine strongly correlative factors of land price of Chengdu city. Secondly, we derived the land price for prediction. Thirdly, we compared the predicted land price with the real land price in the period of 2014-2015. Finally, the comprehensive land price of Chengdu in the period of 2017-2018 was forecasted with random forests and neural network, respectively. According to the results, we found that the error of the random forests is much smaller than that of neural network. Thus, we utilized random forests to predict the comprehensive land price of Chengdu in the period of 2017-2018. Our results showed that the comprehensive land price of Chengdu in the next two years would be stable and rises slightly.

关键词： Forestry Biological neural networks Predictive models Biological system modeling Decision trees Vegetation Monitoring

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Seizing on sparsity in nonlinear hyperspectral unmixing for enhanced image compression

引用

Journal of Applied remote sensing 2016年第4期10卷

作者： Marinoni, Andrea Gamba, Paolo University of Pavia Telecommunications and Remote Sensing Laboratory Department of Electrical Computer and Biomedical Engineering Via Ferrata 5 PaviaI-27100 Italy

Reducing the size of the data on-ground with no information loss represents a strong challenge for the scientific community, since Earth observation (EO) data volumes have strongly and steadily grown during the last 10 years and the need for more efficient compression methods is growing stronger. High-accuracy processing methods employed for EO data understanding and quantifying may result in effective methods for image compression. We propose to use a robust framework of endmember extraction and nonlinear modeling for the on-ground compression of EO data records, where the distribution of the mixture coefficient is exploited to enhance the compression gain while providing high-accuracy reconstruction. Experimental results over real EO datasets show the actual power of the proposed approach. © 2016 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： image compression

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Urban Functional Regions Using Social Media Check-Ins

Urban Functional Regions Using Social Media Check-Ins

引用

IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Zhengqiang Guo Zezhong Zheng Jiaxi Liu Shengli Wang Pingchuan Zhong Mingcang Zhu Yong He Ling Jiang Guoqing Zhou Hongsheng Zhang Jiang Li State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing PRC Center for Information and Geoscience University of Electronic Science and Technology of China Chengdu Sichuan PRC Land and Resources Department of Sichuan Province Chengdu Sichuan PRC Sichuan Institute of Geo-Environment Monitoring Chengdu Sichuan PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin Guangxi PRC Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin New Territories Hong Kong Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

Development of a city cultivates regions with different functions such as working areas and entertainment venues. People in a city usually travel among these regions in certain movement patterns. Identifying those regions will facilitate government management and promote further development of the city. In this paper, we proposed a framework to identify urban functional regions in Chengdu city based upon mobility pattern and point of interest (POIs) information extracted from mobile check-ins data. Firstly, unlike GPS trajectories, location check-ins were discontinuous. Thus, the typical mobility patterns of location check-ins was mined. Secondly, an arrival/departure matrix based on the typical mobility patterns was constructed to obtain the topics of regions by clustering POIs. Because we considered a region's function as our topics, we transferred the problem into a topic modeling problem, and applied an improved probabilistic topic model to infer functions of the regions. We evaluated our approach with 227,428 check-ins in Chengdu collected from Sina Weibo from April 12 2012 to February 16 2013. The results showed that our method outperformed baseline methods solely clustering POIs.

关键词： Urban areas Roads Social network services Trajectory Data mining Global Positioning System Laboratories

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Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy

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Procedia computer Science 2017年 115卷 440-447页

作者： Gajanan M. Galshetwar Laxman M. Waghmare Anil B. Gonde Subrahmanyam Murala Center of Excellence in Signal and Image Processing (COESIP) Department of ECE SGGSIET Nanded Maharashtra 431606 India Computer Vision and Pattern Recognition Laboratory Department of Electrical Engineering IIT Ropar Rupnagar 140001 India

In this paper, a novel approach for content based image retrieval (CBIR) in diabetic retinopathy (DR) is proposed. The concept of salient point selection and inter-plane relationship technique is used. Salient points are selected from edgy image and later using inter-planer relationship, Local Binary Patterns (LBPs) are calculated using the salient point as a center pixel. Our approach enhanced the results as we used color features in combination with LBP features. Experimentation is carried out on MESSIDOR database of 1200 retinal images, proposed approach has average precision of 57.82% as compared to the earlier approach whose average precision is 53.70%.

关键词： Content-Based image retrieval (CBIR) Diabetic Retinopathy (DR) Edgy Salient points Local Binary Patterns (LBPs)

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