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Conference on parallel and distributed methods for image processing

作者： Pycock, D Jackson, RO Univ. of Birmingham (United Kingdom)

ISBN: (纸本)0819425885

Contract-Linda is a novel programming architecture for heterogeneous parallel machines particularly suited to image processing. Previous research has concentrated on static and pre-determined scheduling of computation and on fine grain parallelism. Pre-determined scheduling is satisfactory in cases where the computational process is fully deterministic. However with many image interpretation schemes the flow of control and the nature of the computational procedures can only be determined at run-time. In this paper we describe a programming paradigm for coarse grain and task level parallelism. Task management is based on the Contract Net protocol and utilises the Linda. Coordination Language to provide run-time scheduling. This paradigm accommodates a closely coupled network of heterogeneous processing modules which differ greatly in computational capability;modules based on neural networks, semantic networks, vector and scalar processors are accommodated. Contract;Linda allo-cvs specialised heterogeneous machines to be exploited using a straightforward generic programming model. It does this by providing an internal task management mechanism which ensures that the heterogeneous processing elements are used by the tasks most suited to them and exploits dynamic parallelism within the problem as it is solved. By separating the task of describing the problem from that of describing how the work is carried out on the machine (and providing a solution for this problem) we allow applications to be quickly developed which can effectively utilise specialised machines without the need for specialised programming. We report an experiment to re-implement a cell image interpretation system using Contract-Linda.

关键词： heterogeneous computing parallel computing distributed artificial intelligence image interpretation

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Volume rendering using parallel algebraic logic (PAL) hardware

Volume rendering using parallel algebraic logic (PAL) hardwa...

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Conference on parallel and distributed methods for image processing

作者： Li, HZ Shi, HC Coffield, PC Univ. of Missouri/Columbia (United States) Air Force Wright Lab. (United States)

ISBN: (纸本)0819425885

In this paper, we present the implementation of a volume graphics rendering algorithm using shift-restoration operations on parallel algebraic logic (PAL) image processor. The algorithm is a parallel ray casting algorithm. In order to eliminate shading artifacts caused by inaccurate estimation of surface normal vectors, we use gray level volume instead of binary volume, and apply a low pass filter to smooth the volume object surfaces. By transforming the volume to an intermediate coordinate system to which there is a simple mapping from the object coordinate system, we solve the data redistribution problem caused by nonregular data access patterns in volume rendering. It has been proved very effective in reducing the data communication cost of the rendering algorithm on the PAL hardware.

关键词： volume rendering parallel algorithm mesh processor array

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distributed image edge detection methods and performance

Distributed image edge detection methods and performance

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Proceeedings of the 6th IEEE Symposium on parallel and distributed processing

作者： Zhang, Xiaodong Deng, Hong Univ of Texas at San Antonio San Antonio United States

An edge detection process in computer vision and image processing detects any types of significant features appearing as discontinuities in intensities. This paper presents our experience with parallelizing an edge detection application algorithm that reduces noise and unnecessary detail in a gray-scale image from a coarse level to a fine level of resolution by using an edge focusing technique. Numerical methods and parallel implementations of edge focusing are presented. The image detection algorithms are implemented on three representative massage-passing architectures: a low-cost heterogeneous PVM network, an Intel iPSC/860 hypercube, and a CM-5 massively parallel multicomputer. Our objectives are to provide insight into implementation and performance issues for image processing applications on general-purpose message-passing architectures, to investigate implications on network variations, and to evaluate the computing scalabilities on the three network systems by examining execution and communication patterns of the image edge detection application.

关键词： Edge detection

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Job-shop scheduling applied to computer vision

Job-shop scheduling applied to computer vision

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Conference on parallel and distributed methods for image processing

作者： Sebastian, JM Torres, F Aracil, R Reinoso, O Jimenez, LM Garcia, D Univ. Politecnica de Madrid (Spain) Univ. de Alicante (Spain)

ISBN: (纸本)0819425885

This paper presents a method for minimizing the total elapsed time spent by n tasks running on m differents processors working in parallel. ?he developed algorithm not only minimizes the total elapsed time but also reduces the idle time and waiting time of in-process tasks. This condition is very important in some applications of computer vision in which the time to finish the total process is particularly critical -quality control in industrial inspection, real-time computer vision, guided robots, .... The scheduling algorithm is based on the use of two matrices, obtained from the precedence relationships between tasks, and the data obtained from the two matrixes. The developed scheduling algorithm has been tested in one application of quality control using computer vision. The results obtained have been satisfactory in the application of different image processing algorithms.

关键词： scheduling computer vision real-time parallel processing priority algorithm

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A Review on parallel Medical image processing on GPU 4

A Review on Parallel Medical Image Processing on GPU

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4th International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Khor, Hui Liang Liew, Siau-Chuin Zain, Jasni Mohd. Univ Malaysia Pahang Fac Comp Syst & Software Engn Lebuhraya Tun Razak Kuantan 26300 Pahang Darul Ma Malaysia

ISBN: (纸本)9781467367226

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： parallel computing Medical image processing GPU multicores

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Multi-scale change monitoring of water environment using cloud computing in optimal resolution remote sensing images

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ENERGY REPORTS 2022年 8卷 13610-13620页

作者： Feng, Lei You, Yu Liao, Weiling Pang, Jiawei Hu, Ronghao Feng, Li Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Super Resolut Opt Res Ctr Chongqing 400714 Peoples R China Chongqing Environm & Ecol Big Data Applicat Ctr Chongqing 401121 Peoples R China Chongqing Acad Ecol & Environm Sci Water Environm Engn Technol Innovat Ctr Chongqing 401120 Peoples R China Chongqing Pulusi Environm Protect Technol Dev Co L Chongqing 401121 Peoples R China Chongqing Univ Posts & Telecommun Coll Optoelect Engn Chongqing 400065 Peoples R China

Water is the source of life and a very important part of life. However, the current water resources security is facing global challenges. Affected by climate change and human factors, how to quickly realize the continuous monitoring of water resources changes is crucial to the sustainable management of water resources. In recent years, the amount of multi-temporal remote sensing data has grown rapidly, which has caused problems such as weak single-computer processing for change detection and difficulty in mass image storage management. Traditional single-computer processing methods can no longer meet the needs of remote sensing big data change detection. It provides cutting-edge methods and means for monitoring large-scale changes in water resources. Therefore, a distributed parallel method for remote sensing image change detection based on cloud computing is proposed. Through this method, the change detection efficiency of massive remote sensing images is improved, and the high-resolution remote sensing image change detection based on fully connected conditional random fields is deeply analyzed. By summarizing the experimental data of this method and comparing with other data, it can be seen that when the parallelism is higher, the advantage of running time is more obvious. The performance improvement is also as high as 89.49%. Therefore, distributed parallel processing through cloud computing can complete the efficient change detection of massive remote sensing images without affecting the accuracy. This helps us to better monitor and monitor water resources at multiple scales.

关键词： Cloud computing Remote sensing image Water resource Environmental monitoring

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Kulla, a container-centric construction model for building infrastructure-agnostic distributed and parallel applications

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JOURNAL OF SYSTEMS AND SOFTWARE 2020年 168卷 110665-110665页

作者： Reyes-Anastacio, Hugo G. Gonzalez-Compean, J. L. Sosa-Sosa, Victor J. Carretero, Jesus Garcia-Blas, Javier CINVESTAV Unidad Tamaulipas Km 5-5 Carr Soto la Marina Victoria 87130 Tamaulipas Mexico Univ Carlos III Madrid Comp Sci & Engn Dept Av Univ 30 Madrid 28911 Spain

This paper presents the design, development, and implementation of Kulla, a virtual container-centric construction model that mixes loosely coupled structures with a parallel programming model for building infrastructure-agnostic distributed and parallel applications. In Kulla, applications, dependencies and environment settings, are mapped with construction units called Kulla-Blocks. A parallel programming model enables developers to couple those interoperable structures for creating constructive structures named Kulla-Bricks. In these structures, continuous dataflow and parallel patterns can be created without modifying the code of applications. methods such as Divide&Containerize (data parallelism), Pipe&Blocks (streaming), and Manager/Block (task parallelism) were developed to create Kulla-Bricks. Recursive combinations of Kulla instances can be grouped in deployment structures called Kulla-Boxes, which are encapsulated into VCs to create infrastructure-agnostic parallel and/or distributed applications. Deployment strategies were created for Kulla-Boxes to improve the IT resource profitability. To show the feasibility and flexibility of this model, solutions combining real-world applications were implemented by using Kulla instances to compose parallel and/or distributed system deployed on different IT infrastructures. An experimental evaluation based on use cases solving satellite and medical image processing problems revealed the efficiency of Kulla model in comparison with some traditional state-of-the-art solutions. (C) 2020 Published by Elsevier Inc.

关键词： Infrastructure-agnostic applications Construction model Virtual containers parallel patterns Pipelines

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A rotate-tiling image composition method for parallel volume rendering on distributed memory multicomputers 15

A rotate-tiling image composition method for parallel volume...

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15th International parallel and distributed processing Symposium, IPDPS 2001

作者： Lin, Chin-Feng Yang, Don-Lin Chung, Yeh-Ching Department of Information Engineering Feng Chia University Taichung407 Taiwan

ISBN: (纸本)0769509908

The binary-swap and the parallel-pipelined methods are two popular image composition methods for volume rendering on distributed memory multicomputers. However, these methods either restrict the number of processors to a power of two or require many steps to transform image data that results in high communication overheads. In this paper, we present an efficient image composition method, the rotate-tiling (RT), for parallel volume rendering on distributed memory multicomputers. The RT method can fully utilize all available processors and minimize the communication overheads. In addition, we provide data compression method, the template run-length encoding (TRLE), to further reduce the communication data size. To evaluate the performance of the RT method, we compare the proposed method with the binary-swap method and the parallel-pipelined method. Both theoretical analysis and experimental test are conducted. In the theoretical analysis, we analyze the best performance bound of the RT method in terms of the startup time, the data transmission time, the number of processors, and the number of initial block of a sub-image. In the experimental test, we have implemented these three methods on an SP2 parallel machine. Three volume datasets are used as test samples. The experimental results show that our method outperforms the binary-swap and the parallel-pipelined methods for all test samples and match the results analyzed in the theoretical analysis. For the TRLE method, the experimental results show that the TRLE method can further reduce the composition time for these three methods. © 2001 IEEE.

关键词： Signal encoding

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Optimization of the Functional Decomposition of parallel and distributed Computations in Graph Coloring With the Use of High-Performance Computing

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IEEE ACCESS 2022年 10卷 34996-35011页

作者： Skrinarova, Jarmila Dudas, Adam Matej Bel Univ Fac Nat Sci Dept Comp Sci Banska Bystrica 97401 Slovakia

This article presents methods for correct decomposition for high performance computations related to large sets of graphs. These computations contain large number of calls of sequential, recursive algorithm for NP-complete problem - proper edge coloring of graph. Decomposition of this computational problem is not trivial, since the number of recursions in various parts of the computation is different and causes a high load and time imbalance. We designed, implemented and experimentally verified a new decomposition method that significantly reduces the computational time for a large set of graphs (up to 404 million graphs). This method ensures the same duration of computational time for partial subtasks and thus eliminates the need to wait for synchronization of parallel computations.

关键词： Task analysis parallel processing image color analysis Resource management Program processors NP-complete problem Load management Decomposition distributed computing graph coloring parallel computing snark

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Domain Adaptive Semantic Segmentation via image Translation and Representation Alignment 19

Domain Adaptive Semantic Segmentation via Image Translation ...

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19th IEEE International Symposium on parallel and distributed processing with Applications (IEEE ISPA)

作者： Kang, Jingxuan Zang, Bin Cao, Weipeng Univ Liverpool Sch Comp Sci Liverpool Merseyside England Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781665435741

Domain Adaptation for semantic segmentation is of vital significance since it enables effective knowledge transfer from a labeled source domain (i.e., synthetic data) to an unlabeled target domain (i.e., real images), where no effort is devoted to annotating target samples. Prior domain adaptation methods are mainly based on image-to-image translation model to minimize differences in image conditions between source and target domain. However, there is no guarantee that feature representations from different classes in the target domain can be well separated, resulting in poor discriminative representation. In this paper, we propose a unified learning pipeline, called image Translation and Representation Alignment (ITRA), for domain adaptation of segmentation. Specifically, it firstly aligns an image in the source domain with a reference image in the target domain using image style transfer technique (e.g., CycleGAN) and then a novel pixelcentroid triplet loss is designed to explicitly minimize the intraclass feature variance as well as maximize the inter-class feature margin. When the style transfer is finished by the former step, the latter one is easy to learn and further decreases the domain shift. Extensive experiments demonstrate that the proposed pipeline facilitates both image translation and representation alignment and significantly outperforms previous methods in both GTA5 -> Cityscapes and SYNTHIA -> Cityscapes scenarios.

关键词： Domain Adaptation Semantic Segmentation Style Transfer Centroid Alignment

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