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7th International conference and Exhibition on High-Performance Computing and Networking (HPCN EUROPE 99)

作者： Iikura, M Kobayashi, K Yoshioka, T Ihara, S Nippon Inst Technol Miyashiro Saitama 345 Japan

ISBN: (纸本)3540658211

Recent performance enhancements, upgrade applications, and rapid advances in computers and networking technologies have all enabled information processing devices to share computer resources. We are interested in image processing that requires a large amount of calculations. If individual pixels forming an image can be calculated independently, the processing speed can be increased by parallel processing, thus reducing the overall processing time. However, it has been previously confirmed that increasing the number of servers beyond a certain point does not raise the parallel processing efficiency in a parallel distributed processing environment using a client-server model. Therefore, we constructed a new processing environment using an agent model in order to investigate parallel image processing. This paper discusses the results of experiments in this environment in comparison with those of parallel processing in a client-server model.

关键词： image 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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parallel Matrix Algorithms for image processing

Parallel Matrix Algorithms for Image Processing

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2nd International conference on parallel, distributed, Grid and Cloud Computing for Engineering

作者： Kotas, P. Vondrak, V. Praks, P. Stachon, M. VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Appl Math Ostrava Czech Republic

ISBN: (纸本)9781905088423

The aim of this work is to solve the image retrieval problems with modern methods of numerical linear algebra, which can be easily parallelized for distributed memory architectures like a cluster platform. Algorithm presented in this paper is singular value decomposition (SVD). We show that SVD is directly linked with information retrieval through latent semantic indexing. However, our main concern is efficiency of computations of SVD. We present our parallel implementation of Householder bidiagonalization, which we consider the most computationally demanding step of singular value decomposition. We shall also compare our proposed algorithms with commonly used approaches on the experiments, and we shall emphasize their advatages in sence of usage of standard optimized linear algebra packages.

关键词： singular value decomposition latent semantic indexing matrix algorithms image retrieval

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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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Agent communication based SAR image parallel processing

Agent communication based SAR image parallel processing

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4th International conference on parallel and distributed Computing, Applications and Technologies (PDCAT 2003)

作者： Chi, TH Zhang, X Wu, HQ Fang, JY Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China

ISBN: (纸本)0780378407

Airborne SAR remote sensing image has the characteristic of large data volume and computation burden, so the processing needs very large computer memory and stronger computation ability. Based on the introduction of the SAR image processing procedure, this paper studies the SAR image processing using computer parallel computation technology. The parallel processing mechanism is based on the parallel computer cluster operation and the large virtual shared memory technology. In the Client/Server architecture based SAR image parallel system, agent based network communication plays an important role in the computer performance monitor and burden distribution. In the end, the application of the SAR image parallel processing system in the disaster monitor and assessment system is introduced. The application result illustrates the high efficiency of the system and the feasibility of our research.

关键词： agent SAR image parallel processing disaster monitoring and assessment

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Multiagent boundary detection system: a way to parallel image processing

Multiagent boundary detection system: a way to parallel imag...

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conference on parallel and distributed methods for image processing

作者： Ballet, P Rodin, V Tisseau, J Ecole Nationale d'Ingenieurs de Brest (France)

ISBN: (纸本)0819425885

In this article, we would like to detect boundaries of objects with the help of a multiagent system made up of reactive agents. The reactivity being very important, the agents' behavior is very simple (perception-action) : they have the capacity, nevertheless, to adapt locally to what they consider their environment, that is to say the image. An agent can move and has its own position in its environment. The basic behavior for an agent consists of following the highest brightness gradient and inscribing its path, if estimating to be on an edge, in all the agents' shared memory. Its path thus corresponds to edges which are found in the image. Please note that, in order to be noise resistant, the path is actually stored in the shared memory only if it is long enough. The notion of shared memory is very important because it allows the interaction among agents and the coordination of their actions. The agents actually use already found edges for finding new ones or complete those already detected. We have tested this system on different grey scale images scenes, but as well on synthetic scenes allowing analysis of thus obtained results. The results are promising and especially fast. Our multiagent system has been tested on a single-processor computer, and it has been note that the number of agents in a simulation neither affects the quality of the result nor CPU time necessary for segmentation of a given scene. We think that this approach is original in its use of agents and may be used to implement parallel image processing by assigning, for instance, an agent to each processor.

关键词： Sensors Edge detection image processing image sensors Actuators Computer simulations Computing systems Environmental sensing image analysis image segmentation

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A Novel Approach of Adaptive Thresholding for image Segmentation on GPU 4

A Novel Approach of Adaptive Thresholding for Image Segmenta...

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4th IEEE International conference on parallel, distributed and Grid Computing (IEEE PDGC)

作者： Upadhyay, Pawan Kumar Chandra, Satish Sharma, Arun Jaypee Inst Informat Technol Comp Sci & Engn Noida India SUNY Buffalo Comp Sci & Engn Buffalo NY USA

ISBN: (纸本)9781509036691

In the recent decades, graphical processing unit (GPU) improvises the programmability in the area of image processing for reducing the computational time as compare to CPU. GPUs can solve large data parallel problems and tends to give most robust and energy efficient segmentation method. It also gives the concept of memory pre-allocation and vectorization and handled the large image data set very profoundly for accelerated image segmentation method. In this paper, investigation results are obtained on 100 gold standard dermoscopy images which reveal that the proposed novel method of image segmentation is benefited from GPU processing in terms of speed and accuracy for lesion detection.

关键词： image segmentation Adaptive thresholding GPU Exponential Entropy Dermoscopy images

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Appraisal of Pre-processing Techniques for Automated Detection of Diabetic Retinopathy 5

Appraisal of Pre-processing Techniques for Automated Detecti...

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5th IEEE International conference on parallel, distributed and Grid Computing (PDGC)

作者： Bhardwaj, Charu Jain, Shruti Sood, Meenakshi Jaypee Univ Informat Technol Dept ECE Solan HP India

ISBN: (纸本)9781728106465

Retinopathy is a retinal vascular disease caused by abnormal blood flow that damages the retina and it accounts for 5% of blindness worldwide. There are different causes of Retinopathy as retinopathy of prematurity, diabetic retinopathy and hypertensive retinopathy;this paper mainly focuses on preprocessing techniques employed for the detection of diabetic retinopathy. In this work, literatures reporting different methods for automated diabetic retinopathy detection in color images of retina are analyzed for their strength and shortcoming. Extensive techniques for retinal image screening have been published so far thus this paper presents the evaluation of these methods on a common database of image screening. Different image pre-processing techniques are implemented on fundus images and their performance is compared using performance metrics like mean square error, peak signal to noise ratio and root mean square error. Pre-processing steps used in this work can also be applied to other images of interest to identify and recognize the objects in an image.

关键词： Computer aided screening diabetic retinopathy hypertensive retinopathy retinopathy of prematurity

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An OpenCL framework for high performance extraction of image features

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JOURNAL OF parallel AND distributed COMPUTING 2017年 109卷 75-88页

作者： de Andrade, Douglas Coimbra Trabasso, Luis Gonzaga Petroleo Brasileiro SA Sao Paulo Brazil Aeronaut Inst Technol Mech Engn Div Sao Jose Dos Campos Brazil

image features are widely used for object identification in many situations, including interpretation of data containing natural scenes captured by unmanned aerial vehicles. This paper presents a parallel framework to extract additive features (such as color features and histogram of oriented gradients) using the processing power of GPUs and multicore CPUs to accelerate the algorithms with the OpenCL language. The resulting features are available in device memory and then can be fed into classifiers such as SVM, logistic regression and boosting methods for object recognition. It is possible to extract multiple features with better performance. The GPU accelerated image integral algorithm speeds up computations up to 35x when compared to the single-thread CPU implementation in a test bed hardware. The proposed framework allows real-time extraction of a very large number of image features from full-HD images (better than 30 fps) and makes them available for access in coalesced order by GPU classification algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： OpenCL Heterogeneous programming image descriptors Additive features Haar features Histogram of oriented gradients parallel processing

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