检索结果-内蒙古大学图书馆

计算机应用与软件 2016年第6期33卷 195-199,267页

作者：崔红艳曹建芳忻州师范学院计算机科学与技术系山西忻州034000

针对传统的图像检索方法在处理海量数据时面临的问题,提出一种基于改进的分布式K-Means特征聚类的海量场景图像检索方法。对分布式K-Means算法进行改进,优化了初始聚类中心的选择和迭代过程,并将其应用与场景图像的特征聚类中;充分利用H... 详细信息

针对传统的图像检索方法在处理海量数据时面临的问题,提出一种基于改进的分布式K-Means特征聚类的海量场景图像检索方法。对分布式K-Means算法进行改进,优化了初始聚类中心的选择和迭代过程,并将其应用与场景图像的特征聚类中;充分利用Hadoop分布式平台的海量存储能力和强大并行计算能力,提出了海量场景图像的存储和检索方案,设计了场景图像特征提取、特征聚类以及图像检索三个阶段分布式并行处理的Map和Reduce任务。多组实验表明,提出的方法数据伸缩率曲线平缓,取得了优良的加速比,效率大于0.6,检索的平均准确率达到了88%左右,适合海量场景图像数据的检索。

关键词： Hadoop分布式平台 MapReduce 分布式K-Means算法特征聚类场景图像检索

来源：评论

学校读者我要写书评

暂无评论

OPERATIONAL CLOUD SCREENING SERVICE FOR SENTINEL-2 image TIME SERIES

OPERATIONAL CLOUD SCREENING SERVICE FOR SENTINEL-2 IMAGE TIM...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Gomez-Chova, Luis Amoros-Lopez, Julia Ruiz-Verdu, Antonio Munoz-Mari, Jordi Camps-Valls, Gustau Univ Valencia Image Proc Lab E-46003 Valencia Spain

ISBN: (纸本)9781479979295

This paper deals with the development and implementation of a cloud screening algorithm for image time series, with the focus on the forthcoming Sentinel-2 satellites to be launched under the ESA Copernicus Programme. The proposed methodology is based on kernel ridge regression and exploits the temporal information to detect anomalous changes that correspond to cloud covers. The huge data volumes to be processed when dealing with high temporal, spatial, and spectral resolution datasets motivate the implementation of the algorithm within distributed computer resources. In consequence, an operational cloud screening service has been specifically designed and implemented in the frame of the Sentinels Synergy Framework (SenSyF). The effectiveness of the proposed method is successfully illustrated using a time series dataset with a 5-day revisit derived from SPOT-4 at high resolution, which has been collected by ESA in preparation for the exploitation of the Sentinel-2 mission.

关键词： Sentinel-2 image time series multitemporal analysis change detection cloud masking kernel methods parallel processing grid computing

来源：评论

学校读者我要写书评

暂无评论

A nodal discontinuous Galerkin method for reverse-time migration on GPU clusters

引用

GEOPHYSICAL JOURNAL INTERNATIONAL 2015年第2期203卷 1419-1435页

作者： Modave, A. St-Cyr, A. Mulder, W. A. Warburton, T. Rice Univ Houston TX 77005 USA Shell Global Solut Int BV Rijswijk Netherlands Delft Univ Technol Delft Netherlands

Improving both accuracy and computational performance of numerical tools is a major challenge for seismic imaging and generally requires specialized implementations to make full use of modern parallel architectures. We present a computational strategy for reverse-time migration (RTM) with accelerator-aided clusters. A new imaging condition computed from the pressure and velocity fields is introduced. The model solver is based on a high-order discontinuous Galerkin time-domain (DGTD) method for the pressure-velocity system with unstructured meshes and multirate local time stepping. We adopted the MPI+X approach for distributed programming where X is a threaded programming model. In this work we chose OCCA, a unified framework that makes use of major multithreading languages (e.g. CUDA and OpenCL) and offers the flexibility to run on several hardware architectures. DGTD schemes are suitable for efficient computations with accelerators thanks to localized element-to-element coupling and the dense algebraic operations required for each element. Moreover, compared to high-order finite-difference schemes, the thin halo inherent to DGTD method reduces the amount of data to be exchanged between MPI processes and storage requirements for RTM procedures. The amount of data to be recorded during simulation is reduced by storing only boundary values in memory rather than on disk and recreating the forward wavefields. Computational results are presented that indicate that these methods are strong scalable up to at least 32 GPUs for a three-dimensional RTM case.

关键词： image processing Numerical solutions Computational seismology

来源：评论

学校读者我要写书评

暂无评论

CLASSIFICATION OF TIANGONG-1 HYPERSPECTRAL REMOTE SENSING image VIA CONTEXTUAL SPARSE CODING 14

CLASSIFICATION OF TIANGONG-1 HYPERSPECTRAL REMOTE SENSING IM...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Lv, Qi Dou, Yong Niu, Xin Xu, Jiaqing Xu, Jinbo Natl Univ Def Technol Sch Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781467372206

The hyperspectral remote sensing is one of the frontier techniques in the remote sensing research fields. Applying the sparse coding model to the hyperspectral remote sensing image processing is a hot topic in hyperspectral information processing. To improve the accuracy of hyperspectral image classification, we propose a classification method based on the spatial-spectral joint contextual sparse coding. Firstly, a dictionary is obtained by training using samples selected from the ground-truth reference data. Then, the sparse coefficients of each pixel are calculated based on the learned dictionary. Afterward, the sparse coefficients are input to the classifier and the final classification result is obtained. The visible and near-infrared hyperspectral remote sensing image collected by Tiangong-1 in Chaoyang District of Beijing is used to evaluate the performance of the proposed approach. Experimental results show that the proposed method yields the best classification performance with the overall accuracy of 95.74% and the Kappa coefficient of 0.9476 in comparison with other classification methods.

关键词： Hyperspectral image Remote sensing Sparse coding

来源：评论

学校读者我要写书评

暂无评论

Location-Based parallel Tag Completion for Geo-tagged Social image Retrieval 15

Location-Based Parallel Tag Completion for Geo-tagged Social...

引用

5th ACM International Conference on Multimedia Retrieval (ICMR)

作者： Zhang, Jiaming Wang, Shuhui Huang, Qingming Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781450332743

Benefit from tremendous growth of user-generated content, social annotated tags get higher importance in organization and retrieval of large scale image database on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, existing studies only utilize the relations between visual content and tags to construct global information completion models. In this paper, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship, to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users' tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework which learns the image-tag relation within each POI. To solve the proposed model, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users' tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large scale online image database.

关键词： tag matrix completion geo-location information social image retrieval

来源：评论

学校读者我要写书评

暂无评论

International Conference on image processing and Communications, IP and C 2015

引用

International Conference on image processing and Communications, IPandC 2015

ISBN: (纸本)9783319238135

The proceedings contain 34 papers. The special focus in this conference is on image processing and Communications. The topics include: Classifier selection uses decision profiles in binary classification task;2DHMM-based face recognition method;corner detection based on directional gradients;sensor fusion enhancement for mobile positioning systems;thermal face recognition;feature reduction using similarity measure in object detector learning with haar-like features;assessment of the brain coverage ratio in the postoperative craniosynostosis based on 3D CT scans;combined imaging system for taking facial portraits in visible and thermal spectra;the PUT surveillance database;automatic analysis of vehicle trajectory applied to visual surveillance;algorithmically optimized AVC video encoder with parallel processing of data;neural video compression based on SURF scene change detection algorithm;cell detection in corneal endothelial images using directional filters;the method of probabilistic nodes combination in 2D information retrieval, pattern recognition and biometric modeling;3-D reconstruction of real objects using an android device;methods of natural image preprocessing supporting the automatic text recognition using the OCR algorithms;fast machine vision line detection for mobile robot navigation in dark environments;adjustment of viterbi algorithm for line following robots;studentized range for spatio-temporal track-before-detect algorithm;real-time US image enhancement by forward-backward diffusion using GPU;face-based distributed visitor identification system;color space optimization for lacunarity method in analysis of papanicolaou smears and toward texture-based 3D level set image segmentation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Non-negative Local Coordinate Factorization 1

引用

22nd International Conference on Neural Information processing (ICONIP)

作者： Zhou, Cherong Zhang, Xiang Guan, Naiyang Huang, Xuhui Luo, Zhigang Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China Coll Comp Inst Software Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Dept Comp Sci & Technol Changsha 410073 Hunan Peoples R China

ISBN: (数字)9783319265353

ISBN: (纸本)9783319265353;9783319265346

Non-negative matrix factorization (NMF) is a popular matrix decomposition technique that has attracted extensive attentions from data mining community. However, NMF suffers from the following deficiencies: (1) it is non-trivial to guarantee the representation of the data points to be sparse, and (2) NMF often achieves unsatisfactory clustering results because it completely neglects the labels of the dataset. Thus, this paper proposes a semi-supervised non-negative local coordinate factorization (SNLCF) to overcome the above deficiencies. Particularly, SNLCF induces the sparse coefficients by imposing the local coordinate constraint and propagates the labels of the labeled data to the unlabeled ones by indicating the coefficients of the labeled examples to be the class indicator. Benefit from the labeled data, SNLCF can boost NMF in clustering the unlabeled data. Experimental results on UCI datasets and two popular face image datasets suggest that SNLCF outperforms the representative methods in terms of both average accuracy and average normalized mutual information.

关键词： Non-negative matrix factorization Local coordinate coding Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

parallel convolutional-linear neural network for motor imagery classification 23

Parallel convolutional-linear neural network for motor image...

引用

23rd European Signal processing Conference, EUSIPCO 2015

作者： Sakhavi, Siavash Guan, Cuntai Yan, Shuicheng Brain-Computer Interface Lab Singapore National University of Singapore Department of Electrical and Computer Engineering Learning and Vision Lab Singapore

ISBN: (纸本)9780992862633

Deep learning, recently, has been successfully applied to image classification, object recognition and speech recognition. However, the benefits of deep learning and accompanying architectures have been largely unknown for BCI applications. In motor imagery-based BCI, an energy-based feature, typically after spatial filtering, is commonly used for classification. Although this feature corresponds to the estimate of event-related synchronization/desynchronization in the brain, it neglects energy dynamics which may contain valuable discriminative information. Because traditional classiication methods, such as SVM, cannot handle this dynamical property, we proposed an architecture that inputs a dynamic energy representation of EEG data and utilizes convolutional neural networks for classification. By combining this network with a static energy network, we saw a significant increase in performance. We evaluated the proposed method and compared with SVM on a multi-class motor imagery dataset (BCI competition dataset iv-2a). Our method outperforms SVM with static energy features significantly (p © 2015 EURASIP.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

基于分布式计算的遥感图像水体识别研究

引用

计算机应用与软件 2016年第6期33卷 138-140,145页

作者：杨柳田生伟新疆大学软件学院新疆乌鲁木齐830046

为了提高遥感数据的处理速度,解决遥感信息提取中的数据密集与计算密集问题,将并行计算的思想引入到遥感图像的处理与信息提取中,构建基于Landsat ETM+影像的分布式遥感图像水体提取模型。以渭干河流域为研究区,利用单波段阈值法、多波... 详细信息

为了提高遥感数据的处理速度,解决遥感信息提取中的数据密集与计算密集问题,将并行计算的思想引入到遥感图像的处理与信息提取中,构建基于Landsat ETM+影像的分布式遥感图像水体提取模型。以渭干河流域为研究区,利用单波段阈值法、多波段谱间关系法、水体指数法等方法进行水体信息自动提取的实验。实验结果表明,该模型具有较高的识别精度,能够快速识别水体,并具有稳定的可扩展性和伸缩性。

关键词：大数据遥感水体识别并行计算

来源：评论

学校读者我要写书评

暂无评论

A review on parallel medical image processing on GPU

A review on parallel medical image processing on GPU

引用

International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Hui Liang Khor Siau-Chuin Liew Jasni Mohd Zain Faculty of Computer Systems and Software Engineering University Malaysia Pahang Lebuhraya Tun Razak Malaysia

An efficient implementation are necessary, as most medical imaging methods are computational expensive, and the amount of medical imaging data is growing. Graphic processing units (GPUs) can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. This review investigates the use of GPUs to accelerate medical imaging methods. A set of criteria for efficient use of GPUs are defined. The review concludes that most medical image processing methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.

关键词： Graphics processing units Instruction sets Synchronization Biomedical imaging parallel processing Algorithm design and analysis Biomedical image processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：