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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

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The Comparative Analysis of Chinese GDP Spatialization methods Based on Multi-Sensor Remote Sensing Data 11

The Comparative Analysis of Chinese GDP Spatialization Metho...

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11th International conference on Digital image processing (ICDIP)

作者： Cao, Ziyang Zhao, Yalan Wu, Zhifeng China Transport Telecommun & Informat Ctr Beijing Peoples R China Guangdong Acad Social Sci Guangzhou Peoples R China Guangzhou Univ Sch Geog Sci Guangzhou Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Spatialized Gross Domestic Product (GDP) data was essential for studying the relationship between human activities and environmental changes. Rapid and accurate acquisition of this data was always an important issue. The land use/cover data and the DMSP/OLS nighttime light (NTL) data both had been used to simulate GDP spatialization. By analyzing previous researches, the estimated method based on land use/cover data, estimated method based on radiance-calibrated NTL data and estimated method based on land use/cover data and radiance-calibrated NTL data were applied and compared in this study. The result showed the precision of agricultural production method based on land use/cover data and non-agricultural production method based on radiance-calibrated NTL data which did not include saturated pixels were both high. The accuracy of estimated GDP based on land use/cover data and radiance-calibrated NTL data was the best. The estimated method based on land use/cover data and radiance-calibrated NTL data was used to create a 1-km gridded GDP density map in 2010. In order to make the estimated result more accurate, the county-level statistical data was used to correct it. The corrected 1-km gridded GDP density map in 2010 reflected the Chinese economic development situation and spatial distribution characteristics of GDP density in 2010.

关键词： GDP DMSP-OLS radiance-calibrated nighttime light data land use/cover data spatialization estimated model

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Feature Selection approach using KNN supervised learning for Content-Based image Retrieval 1

Feature Selection approach using KNN supervised learning for...

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1st International conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Alqasemi, Fahd A. Alabbasi, Hamza Q. Sabeha, Fathi G. Alawadhi, Ahmed Kahlid, Sanad Zahary, Ammar University of Science and Technology Yemen Information Technology Department Sanaa Yemen Faculty of Computer and IT Sanaa University And Faculty of Engineering and IT Azal University for Human Development Sanaa Yemen

ISBN: (纸本)9781728144870

Digital images have a serious influence over all the world, since, images have a very tangible importance, which impacts several people needs, this gave image retrieval researches this importance. So, Content-based image retrieval (CBIR) became one of the challenges in information retrieval and image processing fields, its services needs to apply for both business and science domains. In this paper we have proposed a CBIR approach that based on statistical methods feature selection via k-nearest neighbor (KNN) technique. Hence, supervised learning is employed for CBIR to train WANG database images, then the query image was the tested sample, and among predicted class we have filtered the final results. Number of randomly selected query images have been tested via this approach, with a variation of CBIR criteria, which have varied among image types, category query, and threshold distances. The average results have presented in three result groups, some RGB images results have showed good evaluation, in term of precisions measure, also grayscale and RGB images have varied values;on the evaluation of f-measures results, which have changed according some different CBIR factors. Proposed feature selection have made CBIR simpler and effective, this have been showed in high precision evaluation in final results. © 2019 IEEE.

关键词： Supervised learning

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8th Computer Science On-line conference, CSOC 2019

8th Computer Science On-line Conference, CSOC 2019

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8th Computer Science On-line conference, CSOC 2019

ISBN: (纸本)9783030198091

The proceedings contain 120 papers. The special focus in this conference is on Computer Science On-line. The topics include: Cross-collection multi-aspect sentiment analysis;information flow control in interactive analysis of map images with fuzzy elements;a Binary Sine-Cosine Algorithm applied to the Knapsack problem;Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature;model of an intellectual information system for recognizing users of a social network using bioinspired methods;a binary ant lion optimisation algorithm applied to the set covering problem;the minimization of empirical risk through stochastic gradient descent with momentum algorithms;variable step size least mean square optimization for motion artifact reduction: A review;Identification of KDD problems from medical data;Novel optimized filter design for filtered-OFDM to enhance 5G communication spectral efficiency;determination issues of data mining process of failures in the production systems;indonesian food image recognition using convolutional neural network;prevention of local emergencies in the mechanical transport systems;image augmentation techniques for road sign detection in automotive vision system;machine failure prediction technique using recurrent neural network long short-term memory-particle swarm optimization algorithm;assessing the small satellites resilience in conditions of anomalous flight situation;audio gadget recommendation by fuzzy logic;performance analysis of different recurrent neural network architectures and classical statistical model for financial forecasting: A case study on dhaka stock exchange;hybrid algorithm of mobile position-trajectory control;awareness of information and communication technology induced climate change and the developing countries.

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When do neural networks outperform kernel methods? 20

When do neural networks outperform kernel methods?

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Proceedings of the 34th International conference on Neural Information processing Systems

作者： Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari Department of Electrical Engineering Stanford University Department of Statistics University of California Berkeley Department of Statistics Stanford University Department of Electrical Engineering Stanford University and Department of Statistics Stanford University and Google Research Brain Team

ISBN: (纸本)9781713829546

For a certain scaling of the initialization of stochastic gradient descent (SGD), wide neural networks (NN) have been shown to be well approximated by reproducing kernel Hilbert space (RKHS) methods. Recent empirical work showed that, for some classification tasks, RKHS methods can replace NNs without a large loss in performance. On the other hand, two-layers NNs are known to encode richer smoothness classes than RKHS and we know of special examples for which SGD-trained NN provably outperform RKHS. This is true even in the wide network limit, for a different scaling of the *** can we reconcile the above claims? For which tasks do NNs outperform RKHS? If covariates are nearly isotropic, RKHS methods suffer from the curse of dimensionality, while NNs can overcome it by learning the best low-dimensional representation. Here we show that this curse of dimensionality becomes milder if the covariates display the same low-dimensional structure as the target function, and we precisely characterize this tradeoff. Building on these results, we present the spiked covariates model that can capture in a unified framework both behaviors observed in earlier *** hypothesize that such a latent low-dimensional structure is present in image classification. We numerically test this hypothesis by showing that specific perturbations of the training distribution degrade the performances of RKHS methods much more significantly than NNs.

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Towards an Ambient Survey of Consumption Quantities to Support the Nutrition Counseling Process for People affected by the Geriatric Frailty Syndrome

Towards an Ambient Survey of Consumption Quantities to Suppo...

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IEEE International conference on Healthcare Informatics (ICHI)

作者： Patrick Elfert Simon Siggelkow Marco Eichelberg Andreas Hein R&D Devision Health OFFIS Institute for Information Technology Oldenburg Germany Carl von Ossietzky University of Oldenburg Oldenburg Germany

ISBN: (数字)9781728153827

ISBN: (纸本)9781728153834

Stereoscopic cameras allow for the capturing of three-dimensional objects and their volumes in an efficient and ambient way. This work evaluates to what extent this technology can be used to estimate consumption quantities to improve the data basis for nutritionists in the geriatric context for people affected by the frailty syndrome. For this purpose, a system that allows measurements without manual calibrations or additional markers was developed. These measurements result in so-called point clouds that have to be post-processed for further analysis since they they cannot be used directly for the calculation of consumption food quantities. Due to the high dimension of the parameter space of the possible filters and statistical methods and their permutations, a preliminary evaluation is outlined that results in a set of configurations that generally allow for stable measurements. The determined configurations are then further investigated for their accuracy and stability under different lighting conditions and consumption food quantities. Estimated quantities are compared with the plate protocol as one state of the art method in the geriatric and rehabilitation context. The results show that stereoscopic cameras have the potential to be used for the estimation of food intake quantities.

关键词： Employee welfare Three-dimensional displays statistical analysis Stereo image processing Cameras Task analysis Geriatrics

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A Novel Merged Strategy with Deformation Field Reconstruction for Constructing statistical Shape Models

A Novel Merged Strategy with Deformation Field Reconstructio...

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2019 IEEE International conference on Signal, Information and Data processing, ICSIDP 2019

作者： Chen, Zhaofeng Qiu, Tianshuang Wang, Hongkai Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China School of Electronic Engineering Jiujiang University Jiujiang China

ISBN: (纸本)9781728123455

statistical shape models (SSMs) play important roles in the analysis of medical images. SSMs of particular organs are built by learning statistical shape patterns of the target organs from medical images. As a prerequisite step of SSM construction, the anatomical correspondences between the training subjects have to be obtained, requiring nontrivial and tedious organ segmentation from the training images, as well as difficult registration of the segmented organ shapes. To tackle these problems, we design a novel strategy called merged intensity and landmark registration (MILR) which does not require segmentation of the target organ. The MILR method directly registers an organ shape template to the training images by simultaneously optimizing the template-to-image intensity similarity and the key anatomical landmark alignment. Compared to conventional correspondence calculation methods, the MILR method not only saves the time of organ segmentation but also ensures accurate alignment of important anatomical feature points, improving both the efficiency and accuracy of SSM construction. To verify this method, we constructed the human vertebral column SSM from volumetric CT images. We show results of correspondences by three validation metrics anatomical landmark distance (ALD), Dice similarity coefficient (DSC) and average surface distance (ASD)and the corresponding construction of SSMs by generalization ability and specificity of the human vertebral column from the CT images of 17 different subjects. The novel merged strategy, with merged deformation field reconstruction, shows competitive results against the conventional methods. © 2019 IEEE.

关键词： Computerized tomography

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Learning Deep Feature Representation for Face Spoofing 2nd

Learning Deep Feature Representation for Face Spoofing

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2nd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Srinivasa Perumal, R. Santosh, K.C. Chandra Mouli, P.V.S.S.R. School of Information Technology and Engineering Vellore Institute of Technology VelloreTamil Nadu India Department of Computer Science University of South Dakota Vermillion United States Department of Computer Applications National Institute of Technology JamshedpurJharkhand India

ISBN: (纸本)9789811391804

Biometrics is an emerging research area due to its easiness in identification of the person. Face Spoofing is the challenging task in face recognition systems because the human can easily trickster the system by presenting the video or photograph of the person. Many approaches are providing good results in face spoofing, but still it is challenging in intra and cross database validation. Deep learning algorithms have shown significant results in the intra and cross database. This paper used deep learning for extracting the inclusive and favorable features of the person from the face. The extracted features are used for classifying the face image as a real face or genuine face. The performance of the method is evaluated through statistical measures. The experiments were carried out NUAA and CASIA database. The method attained most promising results than other face spoofing methods. © 2019, Springer Nature Singapore Pte Ltd.

关键词： image classification

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Visual-Odometric Localization and Mapping for Ground Vehicles Using SE(2)-XYZ Constraints

Visual-Odometric Localization and Mapping for Ground Vehicle...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Zheng, Fan Liu, Yun-Hui Chinese Univ Hong Kong Stone Robot Inst T Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Shatin Hong Kong Peoples R China VisionNav Robot Ltd Hong Kong Peoples R China

ISBN: (纸本)9781538660263

This paper focuses on the localization and mapping problem on ground vehicles using odometric and monocular visual sensors. To improve the accuracy of vision based estimation on ground vehicles, researchers have exploited the constraint of approximately planar motion, and usually implemented it as a stochastic constraint on an SE(3) pose. In this paper, we propose a simpler algorithm that directly parameterizes the ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected, but incorporated into an integrated noise term of a novel SE(2)-XYZ constraint, which associates an SE(2) pose and a 3D landmark via the image feature measurement. For odometric measurement processing, we also propose an efficient preintegration algorithm on SE(2). Utilizing these constraints, a complete visual-odometric localization and mapping system is developed, in a commonly used graph optimization structure. Its superior performance in accuracy and robustness is validated by real-world experiments in industrial indoor environments.

关键词： Visualization Optimization Land vehicles Perturbation methods Robots Jacobian matrices Sensors

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Higher Order Statistics for Anomaly Detection in Hyper Spectral Imaging 20

Higher Order Statistics for Anomaly Detection in Hyper Spect...

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20th conference on Chemical Biological, Radiological, Nuclear and Explosives (CBRNE) Sensing held at SPIE Defense + Commercial Sensing conference

作者： Languirand, Eric R. Emge, Darren K. Leidos 1504 Quarry Dr Edgewood MD 21040 USA US Army Combat Capabil Dev Command Chem Biol Ctr 8510 Ricketts Point Rd Aberdeen Proving Ground MD 21010 USA

ISBN: (数字)9781510626867

ISBN: (纸本)9781510626867

Hyperspectral imaging (HSI) has become increasingly popular for sensing in defense, commercial, and academic research for its ability to acquire vast amounts of information, relatively quickly, at stand-off distances. As such, the need for rapid or near-real time data reduction is becoming more evident especially when immediate knowledge of the area under investigation is required such as in contested areas, the scene of natural disasters, and other similar scenarios. While analysis of the underlying spectral information may provide specific information about materials present, in HSI determining an anomaly can be just as informative in scenarios such as CB detection for avoidance. Therefore, a rapid, real-time HSI anomaly detection algorithm is merited. In this paper, we present work towards an algorithm for near-real time anomaly detection utilizing higher-order statistics and, in particular, implications due to changes in skewness and kurtosis, the 3rd and 4th central moments. We demonstrate using a visible-SWIR hyperspectral line scanner that anomalies (thiodiglycol and acetaminophen) can be detected in data that is updated to simulate real-time analysis. Changing spectral features result in changes in the probability density function, and can be specifically realized with comparisons of higher order statistics (i.e. skewness and kurtosis), thereby reducing a full spectral analysis at each voxel to a comparison of two values at each pixel. This paper explores utilizing this concept as a means for anomaly detection, evaluating different surfaces that an analyte may be present on, and lastly presents work towards automated background updates for anomaly detection on dynamic surfaces.

关键词： statistical analysis Scanners Data processing Signal to noise ratio Imaging spectroscopy Hyperspectral imaging stochastic processes Detection and tracking algorithms Line scan image sensors Photonics

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SPONTANEOUS AND NON-SPONTANEOUS 3D FACIAL EXPRESSION RECOGNITION USING A statistical MODEL WITH GLOBAL AND LOCAL CONSTRAINTS 25

SPONTANEOUS AND NON-SPONTANEOUS 3D FACIAL EXPRESSION RECOGNI...

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25th IEEE International conference on image processing (ICIP)

作者： Fabiano, Diego Canavan, Shaun Univ S Florida Tampa FL 33620 USA

ISBN: (纸本)9781479970612

In this paper, we propose a novel method for 3D facial expression recognition based on a statistical shape model with global and local constraints. We show that the combination of the global shape of the face, along with local shape index-based information can be used to recognize a range of expressions. These expressions include happiness, sadness, surprise, embarrassment, fear, nervousness, anger, disgust, and pain. We give insights into which features are important for facial expression recognition through statistical analysis. We also show that our proposed method outperforms the current state-of-the-art methods on spontaneous and non-spontaneous facial data.

关键词： Expression classification statistical model spontaneous non-spontaneous

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