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Automated Pseudo-Label Generation and parallel Computing for Enhanced Few-Shot Medical image Segmentation

Automated Pseudo-Label Generation and Parallel Computing for...

引用

2024 Asia Pacific Signal and Information processing Association Annual Summit and conference

作者： Trong-Duc Nguyen Tien-Dung Do Thanh-Ha Do VNU Univ Sci Hanoi Vietnam Post & Telecommun Inst Technol PTIT Hanoi Vietnam

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367331;9798350367348

Few-shot semantic segmentation is a technique with significant potential for medical image segmentation tasks. Most existing few-shot semantic segmentation methods require fully annotated labels for the training process. However, these methods may not be suitable for medical images, where data collection and labeling are challenging. To address this issue, this paper proposed an enhanced, few-shot semantic segmentation model with a new pre-processing step to generate pseudo-labels automatically. In this paper, parallel computing is also developed to accelerate image pre-processing. Experiments done on MRI image datasets present the effectiveness of the new approach since it outperforms conventional few-shot semantic segmentation methods.

关键词： Training Computational modeling Semantic segmentation Magnetic resonance imaging Manuals parallel processing Real-time systems Labeling Optimization Biomedical imaging

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On-Demand processing for Remote Sensing Big Data Analysis 15

On-Demand Processing for Remote Sensing Big Data Analysis

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15th IEEE International Symposium on parallel and distributed processing with Applications (ISPA) / 16th IEEE International conference on Ubiquitous Computing and Communications (IUCC)

作者： Huang, Zhenchun Zhong, Anrun Li, Guoqing Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China

ISBN: (纸本)9781538637906

In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as "big geo data" because of their huge data volume, significant heterogeneity and challenge of fast analysis. In the traditional remote sensing analysis workflows, the data transfer for downloading raw image files to local workstations often costs a lot of time and slows down the data analysis workflows. Because results of remote sensing data analysis models are usually much smaller than raw data to be processed, "on-demand processing", which tries to upload data analysis models and execute them "near" where data stores, can significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework for on-demand remote sensing data analysis is proposed based on three-layered architecture;XML/JSON based runtime environment description;and on-demand model deployment methods. The evaluation on a prototype system shows that on-demand processing framework accelerates the execution of analysis models in 2.8 similar to 12.7 times by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet. By on-demand processing, classical remote sensing data service systems can evolve into remote sensing data processing infrastructures, which provide IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a Service) services, and make it possible to exchange knowledge among scientists by sharing models. Furthermore, a remote sensing data analysis platform for carbon satellites is designed based on the on-demand processing proposed by this paper and will soon be implemented under the support of SunWay-TaihuLight, the world's most powerful super computer.

关键词： remote sensing data analysis on-demand processing big geo data

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BLOCK distributed 3MG ALGORITHM AND ITS APPLICATION TO 3D image RESTORATION

BLOCK DISTRIBUTED 3MG ALGORITHM AND ITS APPLICATION TO 3D IM...

引用

IEEE International conference on image processing (ICIP)

作者： Chalvidal, Mathieu Chouzenoux, Emilie Univ Paris Saclay CVN INRIA Cent Supelec F-91190 Gif Sur Yvette France

ISBN: (纸本)9781728163956

Modern 3D image recovery problems require powerful optimization frameworks to handle high dimensionality while providing reliable numerical solutions in a reasonable time. In this perspective, asynchronous parallel optimization algorithms have received an increasing attention by overcoming memory limitation issues and communication bottlenecks. In this work, we propose a block distributed Majorize-Minorize Memory Gradient (BD3MG) optimization algorithm for solving large scale non-convex differentiable optimization problems. Assuming a distributed memory environment, the algorithm casts the efficient 3MG scheme [1] into smaller dimension subproblems where blocks of variables are addressed in an asynchronous manner. Convergence of the sequence built by the proposed BD3MG method is established under mild assumptions. Application to the restoration of 3D images degraded by a depth-variant blur shows that our method yields significant computational time reduction compared to several synchronous and asynchronous competitors, while exhibiting great scalability potential.

关键词： Majorization-Minimization Block-alternating optimization distributed scheme Asynchronous communication image deblurring Depth-varying blur

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Accelerating the Pre-processing Stages of JPEG Encoder on a Heterogenous System using OpenCL 16

Accelerating the Pre-processing Stages of JPEG Encoder on a ...

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16th IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Alqudami, Nasser Kim, Shin-Dug Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781479986767

Color space conversion and downsampling are among the major computationally intensive steps in typical image and video codec standards, and accelerating these steps will improve the performances of these applications significantly. In this paper, we describe the parallel implementation of the color space conversion and downsampling as pre-processing steps for the JPEG encoder in a heterogeneous environment using the most recent cross-platform Open Computing Language (OpenCL). This work combines a multi-core CPU and a many core GPU in a single solution to perform the computation of the JPEG encoder pre-processing stages. In comparing with CPU based implementation, our OpenCL parallel implementation results in an increase in the speed of the computations by factors of 8.78 on both CPU and GPU devices.

关键词： color conversion downsampling JPEG encoder OpenCL parallel and heterogenous platform

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Study on distributed Complex Event processing in Internet of Things based on Query Plan 5

Study on Distributed Complex Event Processing in Internet of...

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Yuan, Lingyun Xu, Dongdong Ge, Guili Zhu, Mingli Yunnan Normal Univ Coll Informat Sci & Technol Kunming Peoples R China

ISBN: (纸本)9781479987306

Complex event processing is an efficient method in data stream processing of Internet of things, but more of these methods are referred to a single complex event or a small quantity of events. Aiming at this problem, a distributed complex event processing architecture for Internet of things is presented in this paper, in which a distributed query plan of complex event process structure based on directed acyclic graph (DAG) is given, moreover, a distributed query-plan complex-event-processing algorithm based on directed acyclic graph is proposed. The complex tasks are decomposed into several simple sub-tasks which are processed in parallel with the corresponding operator nodes, to realize distributed processing and to improve the efficiency of processing and execution. The simulation results indicate that our method is more efficient in lower RAM consumption, processing time, and others, and the efficiency of data stream processing for Internet of things is improved.

关键词： Internet of things complex event processing query plan distributed parallel processing

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A parallel mutual information based image registration algorithm for applications in remote sensing 4th

引用

4th International Symposium on parallel and distributed processing and Applications

作者： Du, Yunfei Zhou, Haifang Wang, Panfeng Yang, Xuejun Liu, Hengzhu Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783540680673

image registration is a classical problem that addresses the problem of finding a geometric transformation that best aligns two images. Since the amount of multisensor remote sensing imagery are growing tremendously, the search for matching transformation with mutual information is very time-consuming and tedious, and fast and automatic registration of images from different sensors has become critical in the remote sensing framework. So the implementation of automatic mutual information based image registration methods on high performance machines needs to be investigated. First, this paper presents a parallel implementation of a mutual information based image registration algorithm. It takes advantage of cluster machines by partitioning of data depending on the algorithm's peculiarity. Then, the evaluation of the parallel registration method has been presented in theory and in experiments and shows that the parallel algorithm has good parallel performance and scalability.

关键词： image registration

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3D bubbly flow simulation on the GPU - Iterative Solution of a linear system using sub-domain and level-set deflation

3D bubbly flow simulation on the GPU - Iterative Solution of...

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21st Euromicro International conference on parallel, distributed, and Network-Based processing (PDP)

作者： Gupta, Rohit van Gijzen, Martin B. Vuik, Kees Delft Univ Technol Fac EEMCS DIAM Delft Netherlands

ISBN: (纸本)9780769549392;9781467353212

Solving an ill-conditioned linear system with a two level preconditioned Conjugate Gradient method on the GPU presents many options. The viability of these options is studied for different bubbly flow problems. On the basis of experiments conducted, we propose strategies that make our approach computationally suitable. We use the Truncated Neumann series based preconditioning scheme in combination with Deflation for implementing the two-level preconditioned Conjugate Gradient method and test different configurations on a unit cube with varying number of bubbles. Our results exhibit up to an order of magnitude speedup on the GPU. Our preconditioning scheme combined with deflation proves competitive (in terms of computation time and convergence) when compared to deflation with Incomplete Cholesky preconditioning.

关键词： bubbles computational fluid dynamics conjugate gradient methods flow simulation graphics processing units linear systems series (mathematics)

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Hidden Markov Models for Abnormal Event processing in Transportation Data Streams

Hidden Markov Models for Abnormal Event Processing in Transp...

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IEEE 18th International conference on parallel and distributed Systems (ICPADS)

作者： Lau, John Kah-Soon Tham, Chen-Khong Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore

ISBN: (纸本)9781467345651

Making sense of big data and big metadata remains a challenge as more and more data are churned out every day. The problem of adding value to unstructured data requires the application of computationally intensive algorithms to discover useful patterns in the data. In terms of data streams from public transport such as buses, we address the problem of performing time-consuming algorithms to model the data while still being able to process abnormal events in real-time. We propose using Hidden Markov Models (HMMs) for identifying conditions for an abnormal event in bus journeys and methods for isolating HMM computations from real-time event processing. Results show that training HMMs with even noisy metadata can generate models that can recognize an abnormal event in a parallel and distributed manner in the cloud.

关键词： Hidden Markov Models Big Data metadata event processing event-driven architecture public transport

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High Resolution Satellite image processing using Hadoop framework 4

High Resolution Satellite Image Processing using Hadoop fram...

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4th IEEE International conference on Cloud Computing in Emerging Markets (CCEM)

作者： Rajak, Roshan Raveendran, Deepu Chandrasekhar, Maruthi Bh Medasani, Shanti Swarup Uurmi Syst Pvt Ltd Image Understanding Grp Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467385664

Complex image processing algorithms that require higher computational power with large scale inputs can be processed efficiently using the parallel and distributed processing of Hadoop MapReduce Framework. Hadoop MapReduce is a scalable model which is capable of processing petabytes (10(15) order) of data with improved fault tolerance and data parallelism. In this paper we present a MapReduce framework for performing parallel remote sensing satellite data processing using Hadoop and storing the output in HBase. The speedup and performance show that by utilizing Hadoop, we can distribute our workload across different clusters to take advantage of combined processing power on commodity hardware.

关键词： Hadoop HDFS MapReduce image processing Remote Sensing Efficient Satellite data analysis

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Lempel-Ziv data compression on parallel and distributed systems

Lempel-Ziv data compression on parallel and distributed syst...

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1st International conference on Data Compression, Communication, and processing, CCP 2011

作者： De Agostino, Sergio Computer Science Department Sapienza University Rome Italy

ISBN: (纸本)9780769545288

We present a survey of results concerning Lempel-Ziv data compression on parallel and distributed systems, starting from the theoretical approach to parallel time complexity to conclude with the practical goal of designing distributed algorithms with low communication cost. An extension by Storer to image compression is also discussed. © 2011 IEEE.

关键词： Binary images

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