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Conference on parallel and distributed methods for image processing

作者： Assouil, M Deforges, O Kpalma, K Institut National des Sciences Appliquees de Rennes (France)

ISBN: (纸本)0819425885

The use of multiprocessor systems is a well suited solution to handle the problem of implementing realtime image processing applications. We use a multiresolution image decomposition algorithm to show how the A(3) methodology (Algorithm Architecture Adequation), and the CAD software SynDEx which support it, may improve the implementation of such algorithms on a multi-DSP architecture. The application algorithm as well as the hardware are specified with graphs, then the implementation may be formalized in terms of graphs transformations. This methodology reduces significantly the development cycle of image processing applications, by simplifying test and debug process.

关键词： multi-DSP systems algorithm architecture efficient matching multiresolution morphological processing

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Connecting functional brain imaging and parallel distributed processing

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LANGUAGE COGNITION AND NEUROSCIENCE 2015年第4期30卷 380-394页

作者： Cox, Christopher R. Seidenberg, Mark S. Rogers, Timothy T. Univ Wisconsin Dept Psychol Madison WI 53706 USA

Functional neuroimaging and parallel distributed processing (PDP) theory, both introduced to cognitive science in the 1980s, led to influential research programmes that have proceeded in parallel with little mutual influence. The PDP approach advanced specific claims about the nature of neural representations that, perhaps surprisingly, have gone largely untested in functional brain imaging. One reason may be the widespread use of univariate statistical methods for analysing brain imaging data, which typically rely on assumptions that render them unable to detect distributed representations of the kind that PDP predicts. More recent multivariate methods for image analysis may be better suited to detecting such representations. In the current article, we consider why univariate methods have been insufficient to test PDP's representational claims, articulate some of the properties that neural representations ought to have if the PDP view is valid and then survey the recent neuroimaging literature for evidence that neural representations do or do not have these properties. The survey establishes that the PDP view of distributed representations has considerable evidential support. This analysis underscores the importance of understanding how the assumptions underlying methods for analysing functional imaging data constrain the kinds of questions that can be addressed. We then consider the implications for our developing understanding of the neural bases of cognition and for the design of future brain imaging studies.

关键词： fMRI distributed representations MVPA PDP

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High performance medical image processing in client/server-environments

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COMPUTER methods AND PROGRAMS IN BIOMEDICINE 1999年第3期58卷 207-217页

作者： Mayer, A Meinzer, HP H0100 Deutsch Krebsforschungszentrum Dept Med & Biol Informat D-69120 Heidelberg Germany

As 3D scanning devices like computer tomography (CT) or magnetic resonance imaging (MRI) become more widespread, there is also an increasing need for powerful computers that can handle the enormous amounts of data with acceptable response limes. We describe an approach to parallelize some of the more frequently used image processing operators on distributed memory architectures. It is desirable to make such specialized machines accessible on a network, in order to save costs by sharing resources. We present a client/server approach that is specifically tailored to the interactive work with volume data. Our image processing server implements a volume visualization method that allows the user to assess the segmentation of anatomical structures. We can enhance the presentation by combining the volume visualizations on a viewing station with additional graphical elements, which can be manipulated in real-time. The methods presented were verified on two applications for different domains. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved.

关键词： client/server environments image processing volume visualization hybrid visualization parallel processing

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A New Approach for Large-Scale Scene image Retrieval Based on Improved parallel k-Means Algorithm in MapReduce Environment

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-17页

作者： Cao, Jianfang Wang, Min Shi, Hao Hu, Guohua Tian, Yun Xinzhou Teachers Univ Dept Comp Sci & Technol Xinzhou 034000 Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China

The rapid growth of digital images has caused the traditional image retrieval technology to be faced with new challenge. In this paper we introduce a new approach for large-scale scene image retrieval to solve the problems of massive image processing using traditional image retrieval methods. First, we improved traditional k-Means clustering algorithm, which optimized the selection of the initial cluster centers and iteration procedure. Second, we presented a parallel design and realization method for improved k-Means algorithm applied it to feature clustering of scene images. Finally, a storage and retrieval scheme for large-scale scene images was put forward using the large storage capacity and powerful parallel computing ability of the Hadoop distributed platform. The experimental results demonstrated that the proposed method achieved good performance. Compared with the traditional algorithms with single node architecture and parallel k-Means algorithm, the proposed method has obvious advantages for use in large-scale scene image data retrieval in terms of retrieval accuracy, retrieval time overhead, and computational performance (speedup and efficiency, sizeup, and scaleup), which is a significant improvement from applying parallel processing to intelligent algorithms with large-scale datasets.

关键词： image retrieval K-means clustering DIGITAL images image processing PROBLEM solving

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parallel volume rendering using the BSP model

Parallel volume rendering using the BSP model

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Conference on parallel and distributed methods for image processing

作者： Xie, H Li, WQ Murdoch Univ. (Australia) Univ. of Western Australia (Australia)

ISBN: (纸本)0819425885

We present a new parallel volume rendering algorithm based on the split-light model for rendering and the Bulk Synchronous parallel (BSP) model for parallelization. The BSP model provides a simple and architecture-independent approach to structure the parallel program. This parallel program has been tested on a shared memory SGI PowerChallenge machine, a distributed memory IBM SP2 machine and a network of UNIX workstations.

关键词： BSP model volume rendering parallel algorithm architecture-independent parallel programming

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Associative architecture for image processing

Associative architecture for image processing

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Conference on parallel and distributed methods for image processing

作者： Adar, R Akerib, A Associative Computing Ltd. (Israel)

ISBN: (纸本)0819425885

This article presents a new generation in parallel processing architecture for real-time image processing. The approach is implemented in a real time image processor chip, called the Xium(TM)-2, based on combining a fully associative array which provides the parallel engine with a serial RISC core on the same die. The architecture is fully programmable and can be programmed to implement a wide range of color image processing, computer vision and media processing functions in real time. The associative part of the chip is based on patented pending methodology of Associative Computing Ltd. (ACL), which condenses 2048 associative processors, each of 128 ''intelligent'' bits. Each bit can be a processing bit or st memory bit. At only 33 Mhz and 0.6 micron manufacturing technology process, the chip has It computational power of 3 Billion ALU operations per second and 66 Billion string search operations per second. The fully programmable nature of the Xium(TM)-2 chip enables developers to use ACL tools to write their own proprietary algorithms combined with existing image processing and analysis functions from ACL's extended set of libraries.

关键词： parallel processing associative memory associative processing real-time image processing machine vision

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Achieving fast fractal image compression using spiral architecture

Achieving fast fractal image compression using spiral archit...

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International Conference on parallel and distributed processing Techniques and Applications

作者： Liu, D He, XJ Dalian Maritime Univ Inst Naut Sci & Technol Dalian 116026 Liaoning Peoples R China

ISBN: (纸本)1932415610

Spiral Architecture is a relatively new and powerful approach to general-purpose machine vision system. n this novel architecture, Spiral Addition and Multiplication achieve image processing. As we all nown, fractal image compression methods have maximal image compression ratio, at the cost Of slow coding speed. This paper presents an algorithm to achieve high image compression ratio without slow coding speed on Spiral Architecture, which also improves the Spiral Architecture s usage in image processing.

关键词： image compression spiral architecture fractals distributed image processing

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distributed edge detection: Issues and implementations

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IEEE COMPUTATIONAL SCIENCE & ENGINEERING 1997年第1期4卷 72-82页

作者： Zhang, XD Dykes, SG Deng, H University of Texas San Antonio USA Diversifield Technology USA

Experiments in parallelizing an edge detection algorithm on three representative message-passing architectures-a low-cost, heterogeneous PVM network, an Intel iPSC/860 hypercube, and a CM-5 massively parallel multicom... 详细信息

关键词： image edge detection image segmentation image storage Concurrent computing Iterative methods Computer vision Object detection image converters image restoration Application software

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Error Resilient GPU Accelerated image processing for Space Applications

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2018年第9期29卷 1990-2003页

作者： Davidson, R. L. Bridges, C. P. Univ Surrey Surrey Space Ctr Guildford GU2 7XH Surrey England

Significant advances in spaceborne imaging payloads have resulted in new big data problems in the Earth Observation (EO) field. These challenges are compounded onboard satellites due to a lack of equivalent advancement in onboard data processing and downlink technologies. We have previously proposed a new GPU accelerated onboard data processing architecture and developed parallelised image processing software to demonstrate the achievable data processing throughput and compression performance. However, the environmental characteristics are distinctly different to those on Earth, such as available power and the probability of adverse single event radiation effects. In this paper, we analyse new performance results for a low power embedded GPU platform, investigate the error resilience of our GPU image processing application and offer two new error resilient versions of the application. We utilise software based error injection testing to evaluate data corruption and functional interrupts. These results inform the new error resilient methods that also leverages GPU characteristics to minimise time and memory overheads. The key results show that our targeted redundancy techniques reduce the data corruption from a probability of up to 46 percent to now less than 2 percent for all test cases, with a typical execution time overhead of 130 percent.

关键词： GPU parallel processing error resilience EO image processing image compression onboard satellites

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distributed Real-Time image processing of Formation Flying SAR Based on Embedded GPUs

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 6495-6505页

作者： Yang, Tao Xu, Qingbo Meng, Fanteng Zhang, Shuangxi Xidian Univ Sch Aerosp Sci & Technol Xian 710126 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710126 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710071 Peoples R China

Formation flying synthetic aperture radar (FF-SAR) systems, as an important development direction of multichannel SAR, can achieve high-resolution wide-swath imaging. Coherently combining data from satellite receivers puts a strain on the traditional real-time processing systems based on individual satellites. Characteristics, such as the power of real-time on-orbit processing platform, must be properly balanced with constrained memory and parallel computational resources. This article proposes a distributed SAR real-time imaging method based on the embedded graphics processing units (GPUs). The parallel computing method of the chirp scaling algorithm is designed based on the parallel programming model of compute unified device architecture, and the optimization methods of memory and performance are proposed for the hardware architecture of embedded GPUs. In particular, the unified memory management method is used to avoid data copying and communication delays between the CPU and GPU. A hardware verification system for distributed SAR real-time imaging processing based on multiple embedded GPUs is constructed. The proposed algorithm takes 5.86 s to process single-precision floating-point complex imaging with a data size of 8192 x 8192 on a single Jetson Nano platform. The actual power consumption is less than 5 W, and the performance-to-power ratio is greater than 1.7%. The experimental results show that the real-time processing method based on the embedded GPUs proposed in this article has high performance and low-power consumption.

关键词： Graphics processing units Imaging Azimuth Synthetic aperture radar Real-time systems Satellites Radar polarimetry Chirp scaling (CS) algorithm distributed architecture embedded graphics processing unit (GPU) on-orbit real-time processing synthetic aperture radar (SAR)

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