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共 552 条记录，以下是251-260 订阅

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Rapid Tomographic image Reconstruction via Large-Scale parallelization 21st

Rapid Tomographic Image Reconstruction via Large-Scale Paral...

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21st International conference on parallel and distributed Computing (Euro-Par)

作者： Bicer, Tekin Gursoy, Doga Kettimuthu, Rajkumar De Carlo, Francesco Agrawal, Gagan Foster, Ian T. Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Argonne Natl Lab Xray Sci Div Lemont IL USA Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9783662480960;9783662480953

Synchrotron (x-ray) light sources permit investigation of the structure of matter at extremely small length and time scales. Advances in detector technologies enable increasingly complex experiments and more rapid data acquisition. However, analysis of the resulting data then becomes a bottleneck-preventing near-real-time error detection or experiment steering. We present here methods that leverage highly parallel computers to improve the performance of iterative tomographic image reconstruction applications. We apply these methods to the conventional per-slice parallelization approach and use them to implement a novel in-slice approach that can use many more processors. To address programmability, we implement the introduced methods in high-performance MapReduce-like computing middleware, which is further optimized for reconstruction operations. Experiments with four reconstruction algorithms and two large datasets show that our methods can scale up to 8K cores on an IBM BG/Q supercomputer with almost perfect speedup and can reduce total reconstruction times for large datasets by more than 95.4% on 32K cores relative to 1K cores. Moreover, the average reconstruction times are improved from similar to 2h (256 cores) to similar to 1min (32K cores), thus enabling near-real-time use.

关键词： Supercomputers

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

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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.

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

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

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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

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Semi-supervised Non-negative Local Coordinate Factorization 1

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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

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A review on parallel medical image processing on GPU

A review on parallel medical image processing on GPU

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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

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Test oracles based on metamorphic relations for image processing applications

Test oracles based on metamorphic relations for image proces...

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ACIS International conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Tahir Jameel Mengxiang Lin Liu Chao State key Lab of Software Development Environment Beihang University Beijing China

Testing of image processing applications is a challenging job especially, when evaluating the correctness of output image. Generally, output images are evaluated manually by visual inspection carried out by an expert tester, which is the main hindrance in automation of testing process. Recently, statistical and metamorphic testing approaches are presented to automate output evaluation of image processing applications. The statistical method is dependent on availability of statistical distribution of output images, whereas metamorphic testing require more research efforts to make it widely used in practice. Metamorphic testing is a well-known technique to alleviate the test oracle problem and eliminates the required manual efforts by using relations of input and output images. Follow-up test cases are generated based on these relations and their expected output is evaluated. This paper addresses test oracle problem for image processing applications and demonstrates how properties of implementation under test can be adopted as metamorphic relations. We have studied general and specific metamorphic relations of morphological image operations such as dilation and erosion. Selection of metamorphic relations and their effectiveness by mutation analysis is demonstrated. The results show that metamorphic testing is useful for evaluation of output images in the absence of a perfect test oracle.

关键词： Testing image processing Software algorithms Standards Smoothing methods Noise Software

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Model-based estimation of microscopic anisotropy in macroscopically isotropic substrates using diffusion MRI 24th

Model-based estimation of microscopic anisotropy in macrosco...

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24th International conference on Information processing in Medical Imaging, IPMI 2015

作者： Ianuş, Andrada Drobnjak, Ivana Alexander, Daniel C. Centre for Medical Image Computing University College London London United Kingdom

Non-invasive estimation of cell size and shape is a key challenge in diffusion MRI. Changes in cell size and shape discriminate functional areas in the brain and can highlight different degrees of malignancy in cancer tumours. Consequently various methods have emerged recently that aim to measure the microscopic anisotropy of porous media such as biological tissue and aim to reflect pore eccentricity, the simplest shape feature. However, current methods assume a substrate of identical pores, and are strongly influenced by non-trivial size distribution. This paper presents a model-based approach that provides estimates of pore size and shape from diffusion MRI data. The technique uses a geometric model of randomly oriented finite cylinders with gamma distributed radii. We use Monte Carlo simulation to generate synthetic data in substrates consisting of randomly oriented cuboids with various size distributions and eccentricities. We compare the sensitivity of single and double pulsed field gradient (sPFG and dPFG) sequences to the size distribution and eccentricity and further compare different protocols of dPFG sequences with parallel and/or perpendicular pairs of gradients. The key result demonstrates that this model-based approach can provide features of pore shape (specifically eccentricity) that are independent of the size distribution unlike previous attempts to characterise microscopic anisotropy. We show further that explicitly accounting for size distribution is necessary for accurate estimates of average size and eccentricity, and a model that assumes a single size fails to recover the ground truth values. We find the most accurate parameter estimates for dPFG sequences with mixed parallel and perpendicular gradients, nevertheless all other sequences, including sPFG, show sensitivity as well. © Springer International Publishing Switzerland 2015.

关键词： Magnetic resonance imaging

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Real-Time Depth-image-Based Rendering on GPU

Real-Time Depth-Image-Based Rendering on GPU

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International conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC

作者： Zengzeng Sun Cheolkon Jung Samsung Electronics SRC-Xian DMC Lab/SSG Xi'an China School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9781467392013

In this paper, we propose real-time depth-image-based rendering (DIBR) on GPU for 1280×720 resolution. We utilize depth adaptive preprocessing and super-resolution to achieve high-quality DIBR. Moreover, we employ GPU-based parallel computing to achieve real-time DIBR. Experimental results demonstrate that the proposed method achieves superior performance in comparison with the existing methods with respect to rendering quality and computing time.

关键词： Three-dimensional displays Graphics processing units Rendering (computer graphics) Instruction sets Real-time systems Videos Filling

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Classification of Tiangong-1 hyperspectral remote sensing image via contextual sparse coding

Classification of Tiangong-1 hyperspectral remote sensing im...

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International conference on Machine Learning and Cybernetics (ICMLC)

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Jinbo Xu School of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467372220

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 join-t 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 Sparse coding Classification methods remote sensing hyperspectral remote sensing hyperspectral imagery image classification

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Robust weighted supervised sparse coding for image classification

Robust weighted supervised sparse coding for image classific...

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International conference on Communication Technology (ICCT)

作者： Xiang Zhang Naiyang Guan Zhigang Luo College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467370066

Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved the state-of-the-art results in image classification. However, they suffer from poor robustness shortcomings in practice. This paper proposes a robust weighted supervised sparse coding method (RWSSC) to address this deficiency. Particularly, RWSSC distinguishes different classes' contributions to the sparse coding by a novel weighting strategy meanwhile removes the out liers by imposing l1-regularization over the noisy entries. Benefitting from these strategies, RWSSC can effectively boost performance of sparse coding in image classification. Besides, we developed the block coordinate descent algorithm to optimize it, and proved its convergence. Experimental results of image classification on two popular datasets show that RWSSC outperforms the representative sparse coding methods in quantities.

关键词： Support vector machines Robustness Classification algorithms Dictionaries Manganese

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