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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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A parallel Segmentation after Classification Algorithm of Multi-Spectral image of K-Means of Deep Learning and Panchromatic Based On Wavelet 11

A Parallel Segmentation after Classification Algorithm of Mu...

引用

11th International conference on Digital image processing (ICDIP)

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Song, Donghong Xue, Xiaoyong Xi An Jiao Tong Univ Dept Comp Sci & Technol & AI Xian 710049 Shaanxi Peoples R China Anyang Normal Univ Sch Comp & Informat Engn Anyang 455000 Henan Peoples R China Guangzhou Univ South China Inst software Engn Dept AI & Software Engn Guangzhou 510990 Guangdong Peoples R China Middle Sch Luonan Cty Shangluo 726100 Shaanxi Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

[Purpose]To take the advantages of a variety of remote sensing data, the application of remote sensing image classification is a very important choice. Remote sensing image classification is large in computing capacity and time-consuming, and with the development of modern remote sensing technology, the amount of various remote sensing data obtained is getting larger and larger, the issue of how to fuse remote sensing image quickly and accurately and of getting useful information is becoming more and more urgent especially in some remote sensing applications such as disaster monitoring, prevention and relief, etc. In this paper, in order to fuse remote sensing image quickly and accurately, a parallel classification algorithm of multi-spectral image and panchromatic image based on wavelet transform is proposed.[methods] In the method, based on parallel computing, the low-frequency components of wavelet decomposition are fused with the classification rule based on the feature matching, and the high-frequency components of wavelet decomposition are fused with the classification rule based on the sub-region variance. Then the low-frequency components and the high-frequency components after classification are processed with the inverse wavelet transform, and the fused image is obtained. According to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis, an efficient image segmentation method based on Deep Learning Semantic analysis and wavelet transform is proposed to achieve precision of classification.[Results] The experiment results show that the proposed method can get better classification results and faster computing speed for multi-spectral image and panchromatic image. Originality. In the proposed classification algorithm of multi-spectral image and panchromatic image, wavelet transform and different proper classification rules for low-frequency components and high-frequency components of wavelet decomposition are used.

关键词： remote sensing image classification panchromatic image multi-spectral image wavelet transform parallel computing Data clustering

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Virtual-Real Fusion processing Based on parallel Distribution 14th

Virtual-Real Fusion Processing Based on Parallel Distributio...

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14th conference on image and Graphics Technologies and Applications, IGTA 2019

作者： Zhai, Hongyu Zhang, Tongtong Xu, Richeng School of Computer Science and Technology Changchun University of Science and Technology ChangchunJilin130022 China

ISBN: (纸本)9789811399169

For the demand of domestic digital film industry to improve the quality and efficiency of special effects film and television works, This paper studies the transmission quality of virtual and real fusion and the fusion efficiency, and proposes a better video transmission method combined with virtual and real fusion technology, which made the digital video industry more complete. Firstly, the Hadoop platform is built as the basis of distributed transmission, and then the parallel distributed transmission method and the virtual and real fusion technology proposed in this paper are combined to complete the experiment. In this paper, the efficiency of this method is compared with other existing advanced transmission methods. The experimental results show that the efficiency and transmission quality are improved compared with other methods. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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An Efficient Work-Stealing Scheduler for Task Dependency Graph

An Efficient Work-Stealing Scheduler for Task Dependency Gra...

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International conference on parallel and distributed Systems (ICPADS)

作者： Chun-Xun Lin Tsung-Wei Huang Martin D. F. Wong University of Illinois Urbana-Champaign Urbana IL USA The University of Utah Salt Lake City Utah

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Work-stealing is a key component of many parallel task graph libraries such as Intel Threading Building Blocks (TBB) FlowGraph, Microsoft Task parallel Library (TPL) Batch .Net, Cpp-Taskflow, and Nabbit. However, designing a correct and effective work-stealing scheduler is a notoriously difficult job, due to subtle implementation details of concurrency controls and decentralized coordination between threads. This problem becomes even more challenging when striving for optimal thread usage in handling parallel workloads with complex task graphs. As a result, we introduce in this paper an effective work-stealing scheduler for execution of task dependency graphs. Our scheduler adopts a simple and efficient strategy to adapt the number of working threads to available task parallelism at any time during the graph execution. Our strategy is provably good in preventing resource underutilization and simultaneously minimizing resource waste when tasks are scarce. We have evaluated our scheduler on both micro-benchmarks and a real-world circuit timing analysis workload, and demonstrated promising results over existing methods in terms of runtime, energy efficiency, and throughput.

关键词： Runtime parallel processing Throughput Libraries Energy efficiency Timing Task analysis

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Evaluation Through Realistic Simulations of File Replication Strategies for Large Heterogeneous distributed Systems 24th

Evaluation Through Realistic Simulations of File Replication...

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

作者： Chai, Anchen Camarasu-Pop, Sorina Glatard, Tristan Benoit-Cattin, Hugues Suter, Frederic Univ Lyon 1 Univ Lyon CNRS INSERMCREATISINSA LyonUMR5220U1044 Lyon France CNRS IN2P3 Comp Ctr Lyon France INRIA Lyon France Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada

ISBN: (纸本)9783030105495;9783030105488

File replication is widely used to reduce file transfer times and improve data availability in large distributed systems. Replication techniques are often evaluated through simulations, however, most simulation platform models are oversimplified, which questions the applicability of the findings to real systems. In this paper, we investigate how platform models influence the performance of file replication strategies on large heterogeneous distributed systems, based on common existing techniques such as prestaging and dynamic replication. The novelty of our study resides in our evaluation using a realistic simulator. We consider two platform models: a simple hierarchical model and a detailed model built from execution traces. Our results show that conclusions depend on the modeling of the platform and its capacity to capture the characteristics of the targeted production infrastructure. We also derive recommendations for the implementation of an optimized data management strategy in a scientific gateway for medical image analysis.

关键词： File replication Platform model Realistic simulation Evaluation

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An Improved DV-hop algorithm based on iterative computation and two communication ranges for sensor network localization 20

An Improved DV-hop algorithm based on iterative computation ...

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20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Xiu, Xu Changzheng, Zhu School of Computer Science and Technology China University of Mining and Technology Xuzhou Jiangsu221000 China

ISBN: (纸本)9781728126166

In order to improve the location accuracy of DV-Hop algorithm, an improved algorithm based on iterative computation and two communication ranges was proposed. This algorithm first selects a suitable communication radius for the current network topology and then uses it to estimate the average per hop distance of beacon nodes with the default communication radius of the node. Finally, an iterative algorithm was used to revise the average per hop distance obtained in the previous step so as to select the minimum average per hop distance to calculate the distance between the unknown nodes and beacon nodes. The simulation result indicates that the improved algorithm can greatly improve the location accuracy without obviously increasing algorithm complexity and communication traffic. © 2019 IEEE.

关键词： Iterative methods

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Performance Optimizations and Analysis of distributed Deep Learning with Approximated Second-Order Optimization Method 19

Performance Optimizations and Analysis of Distributed Deep L...

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48th International conference on parallel processing (ICPP)

作者： Tsuji, Yohei Osawa, Kazuki Ueno, Yuichiro Naruse, Akira Yokota, Rio Matsuoka, Satoshi Tokyo Inst Technol Tokyo Japan NVIDIA Tokyo Japan AIST AIST Tokyo Tech RWBC OIL Tokyo Inst Technol Global Sci Informat & Comp Ctr Tokyo Japan Tokyo Inst Technol RIKEN Ctr Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9781450371964

Faster training of deep neural networks is desired to speed up the research and development cycle in deep learning. distributed deep learning and second-order optimization methods are two different techniques to accelerate the training of deep neural networks. In the previous work, researchers show that an approximated second-order optimization method, called K-FAC, can mitigate each other drawbacks of the two techniques. However, there was no detailed discussion on the performance, which is critical for the usage in practice. In this work, we propose several performance optimization techniques to reduce the overheads of K-FAC and to accelerate the overall training. Applying all performance optimizations, we are able to speed up the training 1.64 times per iteration compared to a baseline. Additional to the performance optimizations, we construct a simple performance model to predict model training performance to help the users to determine whether distributed K-FAC is appropriate or not for their training in terms of wall-time.

关键词： deep learning second-order optimization neural networks

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Fire Frontline Monitoring by Enabling UAV-Based Virtual Reality with Adaptive Imaging Rate 53

Fire Frontline Monitoring by Enabling UAV-Based Virtual Real...

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53rd Asilomar conference on Signals, Systems, and Computers (ACSSC)

作者： Islam, Shafkat Huang, Qiyuan Afghah, Fatemeh Fule, Peter Razi, Abolfazl No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA

ISBN: (纸本)9781728143002

Recently, using drones for forest fire management has gained a lot of attention from the research community due to their advantages such as low operation and deployment cost, flexible mobility, and high-quality imaging. It also minimizes human intervention, especially in hard-to-reach areas where the use of ground-based infrastructure is troublesome. Drones can provide virtual reality to firefighters by collecting on-demand high-resolution images with adjustable zoom, focus, and perspective to improve fire control and eliminate human hazards. In this paper, we propose a novel model for fire expansion as well as a distributed algorithm for drones to relocate themselves towards the front-line of an expanding fire field. The proposed algorithm comprises a light-weight image processing for fire edge detection that is highly desirable over computational expensive deep learning methods for resource-constrained drones. The positioning algorithm includes motions tangential and normal to fire frontline to follow the fire expansion while keeping minimum pairwise distances for collision avoidance and non-overlapping imaging. We proposed an action-reward mechanism to adjust the drones' speed and processing rate based on the fire expansion rate and the available onboard processing power. Simulations results are provided to support the efficacy of the proposed algorithm.

关键词： UAV networks fire monitoring virtual reality autonomous control image-based edge detection

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distributed Surface Reconstruction from Point Cloud for City-Scale Scenes 7

Distributed Surface Reconstruction from Point Cloud for City...

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7th International conference on 3D Vision (3DV)

作者： Han, Jiali Shen, Shuhan Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728131313

image based 3D modeling is an effective way to reconstruct large-scale scenes, especially city-level scenarios. In the image based modeling pipeline, obtaining a watertight mesh model from a noisy multiple view stereo point cloud is a key step to ensure the model quality. However, state-of-the-art method [34] relies on the global Delaunay-based optimization formed by all points and cameras, and will encounter scale problem when dealing with large scenes. To circumvent this limitation, this paper proposes a distributed surface reconstruction approach which could handle city-scale scenes with limited memory and time consumption. Firstly, the whole scene is adaptively divided into several chunks with overlapping boundaries, and each chunk can satisfy the memory limit. Then, the Delaunay-based optimization is performed to extract meshes for each chunk in parallel. Finally, the local meshes are merged together by resolving local inconsistencies in the overlapping areas. We test the proposed method on three city-scale scenes with billions of points and tens of thousands of images, and demonstrate its scalability and completeness compared with the state-of-the-art methods.

关键词： Three-dimensional displays Surface reconstruction image reconstruction Optimization Merging Surface treatment Memory management

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Robust Target Localization in distributed MIMO Radars Based on Iterative Reweight Least Squares

Robust Target Localization in Distributed MIMO Radars Based ...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Zehua Yu Jun Li Qinghua Guo Xinke Li Hailong Kang National Laboratory of Radar Signal Processing Xidian University Xi’an China School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

In this paper, a robust target localization method based on iterative reweight least squares (IRWLS) is presented to against outliers in distributed MIMO radars. Unlike conventional weight least squares model which derived from the maximum likelihood estimator (MLE), the proposed robust global cost function is established based on least absolute model. Since the least absolute is nontrivial to solve directly, we convert it into a reweight least squares problem. After that, we linearize the relation between target location and bistatic range (BR) without introducing any nuisance parameters, which is different from existing linearization methods. The solution of proposed method can be obtained efficiently via iteration with closed-form expression. Simulation results validate the robust performance of proposed method under low SNR condition and outliers.

关键词： Location awareness Maximum likelihood estimation Closed-form solutions MIMO radar Simulation Cost function Signal to noise ratio

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Enhanced Sparse Model for Blind Deblurring 16th

Enhanced Sparse Model for Blind Deblurring

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16th European conference on Computer Vision, ECCV 2020

作者： Chen, Liang Fang, Faming Lei, Shen Li, Fang Zhang, Guixu Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China School of Software Engineering East China Normal University Shanghai China School of Mathematical Sciences East China Normal University Shanghai China

ISBN: (纸本)9783030585945

Existing arts have shown promising efforts to deal with the blind deblurring task. However, most of the recent works assume the additive noise involved in the blurring process to be simple-distributed (i.e. Gaussian or Laplacian), while the real-world case is proved to be much more complicated. In this paper, we develop a new term to better fit the complex natural noise. Specifically, we use a combination of a dense function (i.e. l2) and a newly designed enhanced sparse model termed as le, which is developed from two sparse models (i.e. l1 and l0), to fulfill the task. Moreover, we further suggest using le to regularize image gradients. Compared to the widely-adopted l0 sparse term, le can penalize more insignificant image details (Fig. 1). Based on the half-quadratic splitting method, we provide an effective scheme to optimize the overall formulation. Comprehensive evaluations on public datasets and real-world images demonstrate the superiority of the proposed method against state-of-the-art methods in terms of both speed and accuracy. © 2020, Springer Nature Switzerland AG.

关键词： Additive noise

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