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parallel and distributed methods for image processing

作者： Lückenhaus, Maximilian Eckstein, Wolfgang Institute of Computer Science Munich University of Technology Orleansstr. 34 D-81667 München Germany

parallelization of image analysis tasks forms a basic key for processing huge image data in real time. At this, suitable subtasks for parallel processing have to be extracted and mapped to components of a distributed system. Basically, this task should be done by the processing system and not by the user, as automatical parallelization allows a flexible resource management and reduces time for developing image analysis programs. This paper describes a multi-agent based system for planning and performing image analysis tasks within a distributed system. It illustrates a method for modeling image analysis tasks under the viewpoint of parallel processing and explains the special design requirements for parallelizing agents. Furthermore, we describe concepts for agent cooperation and for using the agent's ability of learning to allow long term improvement of its planning and scheduling strategies. The presented image analysis system allows an architecture-independent parallel processing of image analysis tasks with an optimized resource management. ©2005 Copyright SPIE - The International Society for Optical Engineering.

关键词： Multi agent systems

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Distribution of image processing applications on a heterogeneous workstation network. Modeling, load-balancing and experimental results.

Distribution of image processing applications on a heterogen...

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Conference on parallel and distributed methods for image processing

作者： HernandezSosa, D CabreraGamez, J Univ. de Las Palmas de Gran Canaria (Spain)

ISBN: (纸本)0819425885

This work analyzes the computation distribution in applications generated by a multilevel knowledge-based system for image processing called SVEX.(1) This distribution has been carried out on a heterogeneous workstation network, trying to take advantage of the availability and frequent infra-utilization of this computational resource. The parallelization is based on message-passing tool parallel Virtual Machine (PVM).(2) Firstly SVEX and its computational scheme are described, detailing the structure of the first level (the Pixel Processor). Then different distribution paradigms are studied, selecting for its implementation the parallelism based on the data. Considering this alternative, the research addresses two fundamental problems: analysis of basic load-balancing schemes and obtaining-a model for predicting parallelization behavior as new machines are added to the computational network. The results produced in a series of experiments permit the comparison of load-balancing schemes and the validation of the proposed model. The experiments include the processing of both static images and sequences.

关键词： knowledge based vision systems (KBVS) parallel Virtual Machine (PVM) distributed image processing load-balancing

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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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Communication in a large DSP array for image processing

Communication in a large DSP array for image processing

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Conference on parallel and distributed methods for image processing

作者： Ercan, MF Fung, XF Demokan, MS Hong Kong Polytechnic Univ. (Hong Kong)

ISBN: (纸本)0819425885

The communication overhead in many multiprocessor computing platform is a critical factor over performance. In this paper we will present communication performance of a large processing array built with TI 320C40 DSPs. Inter-processor communication is provided by message passing which is a common method used in multiprocessors systems. The system is developed for image processing therefore transmission of large data blocks and various forms of communication are required frequently. The processor used in this system has six built in communication links. They are X-bit, bi-directional links with a speed of 20 Mbytes/sec. A processing array built with these processors employs MIMD paradigm and static interconnection. In this paper, the communication performance of such DSP network is investigated and performance results are presented. The communication functions include broadcasting, scattering, gathering and point to point transmission of messages.

关键词： inter processor communication distributed memory systems parallel processing

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Exploring parallel capabilities of an innovative numerical method for recovering image velocity vectors field

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MATHEMATICAL AND COMPUTER MODELLING 2010年第3-4期51卷 138-143页

作者： Francomano, E. Macaluso, C. Tortorici, A. Toscano, E. Univ Palermo Dipartimento Ingn Informat I-90128 Palermo Italy

In this paper an efficient method devoted to estimate the velocity vectors field is investigated. The method is based on a quasi-interpolant operator and involves a large amount of computation. The operations characterizing the computational scheme are ideal for parallel processing because they are local, regular and repetitive. Therefore, the spatial parallelism of the process is studied to rapidly proceed in the computation on distributed multiprocessor systems. The process has shown to be synchoronous, with good task balancing and requiring a small amount of data transfer. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： image velocity vectors field Quasi-interpolant operator B-spline functions distributed multiprocessor systems

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A distributed AND ITERATIVE METHOD FOR SQUARE-ROOT FILTERING IN SPACE-TIME ESTIMATION

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AUTOMATICA 1995年第1期31卷 67-82页

作者： CHIN, TM KARL, WC WILLSKY, AS MIT INFORMAT & DECIS SYST LABCAMBRIDGEMA 02139 MIT DEPT ELECT ENGN & COMP SCICAMBRIDGEMA 02139

We describe a distributed and iterative approach to perform the unitary transformations in the square root information filter implementation of the Kalman filter, providing an alternative to the common QR factorization-based approaches. The new approach is useful in approximate computation of filtered estimates for temporally evolving random fields defined by local interactions and observations. Using several examples motivated by computer vision applications, we demonstrate that near-optimal estimates can be computed for problems of practical importance using only a small number of iterations, which can be performed in a finely parallel manner over the spatial domain of the random field.

关键词： KALMAN FILTERS image processing MULTIDIMENSIONAL SYSTEMS MODEL REDUCTION parallel processing ITERATIVE methods

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GPU Implementation of Bitplane Coding with parallel Coefficient processing for High Performance image Compression

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2017年第8期28卷 2272-2284页

作者： Enfedaque, Pablo Auli-Llinas, Francesc Moure, Juan Carlos Univ Autonoma Barcelona Dept Informat & Commun Engn E-08193 Barcelona Spain Univ Autonoma Barcelona Dept Comp Architecture & Operating Syst E-08193 Barcelona Spain

The fast compression of images is a requisite in many applications like TV production, teleconferencing, or digital cinema. Many of the algorithms employed in current image compression standards are inherently sequential. High performance implementations of such algorithms often require specialized hardware like field integrated gate arrays. Graphics processing Units (GPUs) do not commonly achieve high performance on these algorithms because they do not exhibit fine-grain parallelism. Our previous work introduced a new core algorithm for wavelet-based image coding systems. It is tailored for massive parallel architectures. It is called bitplane coding with parallel coefficient processing (BPC-PaCo). This paper introduces the first high performance, GPU-based implementation of BPC-PaCo. A detailed analysis of the algorithm aids its implementation in the GPU. The main insights behind the proposed codec are an efficient thread-to-data mapping, a smart memory management, and the use of efficient cooperation mechanisms to enable inter-thread communication. Experimental results indicate that the proposed implementation matches the requirements for high resolution (4 K) digital cinema in real time, yielding speedups of 30x with respect to the fastest implementations of current compression standards. Also, a power consumption evaluation shows that our implementation consumes 40x less energy for equivalent performance than state-of-the-art methods.

关键词： image coding SIMD computing graphics processing unit (GPU) compute unified device architecture (CUDA)

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NPU-based image compositing in a distributed visualization system

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2007年第4期13卷 798-809页

作者： Pugmire, David Monroe, Laura Davenport, Carolyn Connor DuBois, Andrew DuBois, David Poole, Stephen Los Alamos Natl Lab Los Alamos NM 87545 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

This paper describes the first use of a Network processing Unit (NPU) to perform hardware-based image composition in a distributed rendering system. The image composition step is a notorious bottleneck in a clustered rendering system. Furthermore, image compositing algorithms do not necessarily scale as data size and number of nodes increase. Previous researchers have addressed the composition problem via software and/or custom-built hardware. We used the heterogeneous multicore computation architecture of the Intel IXP28XX NPU, a fully programmable commercial off-the-shelf (COTS) technology, to perform the image composition step. With this design, we have attained a nearly four-times performance increase over traditional software-based compositing methods, achieving sustained compositing rates of 22-28 fps on a 1, 024 x 1, 024 image. This system is fully scalable with a negligible penalty in frame rate, is entirely COTS, and is flexible with regard to operating system, rendering software, graphics cards, and node architecture. The NPU-based compositor has the additional advantage of being a modular compositing component that is eminently suitable for integration into existing distributed software visualization packages.

关键词： hardware-assisted image compositing high-performance computing image compositing Network processing Unit parallel rendering PC clusters visualization volume rendering

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Implementation of two-dimension FFT on massively parallel processing system DAWN1000

Implementation of two-dimension FFT on massively parallel pr...

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Conference on parallel and distributed methods for image processing

作者： Wang, YT Wang, ZS Institute of Electronics (China)

ISBN: (纸本)0819425885

Two-dimensional (2D) Discrete Fourier Transform (DFT) frequently needs to be performed in the digital image processing. Although the computing time of 2D DFT can be dramatically reduced by using 2D Fast Fourier Transform (FFT), the processing speed of a very large array is yet intolerable. The development of parallel processing system promotes the application of 2D FFT. In this paper, we present the implementation of 2D FFT as a general procedure by row-column method and vector-radix method based on a general-purpose massively parallel processing system-DAWN 1000 developed in China. Even though the 2D FFT has parallel characteristics in nature, the requirement of corner-turning and the existence of data communication make its implementation more complicated. We analyze the impact of the machine capacity and the computing complexity on the algorithm efficiency and evaluate the implementation in terms of the arithmetic operations as well as the data transfer. The comparison of the two methods shows the fact that each method has its own advantages and disadvantages. Combining their traits, we design a new implementation algorithm concerning its flexibility, the efficiency and the complexity of the communication. As an example, we fulfill the spaceborne SAR image processing by using the new approach.

关键词： parallel processing MPP row-column algorithm vector-radix algorithm block algorithm two-dimensional FFT SAR image processing

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An overview of AIM: Supporting computer vision on heterogeneous high performance computing systems

An overview of AIM: Supporting computer vision on heterogene...

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SPIE Conference on parallel and distributed methods for image processing II

作者： Sweat, M Wilson, JN Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)0819429074

The AIM (Adaptive image Manager) is a client/server based system providing a computer vision and image processing specific protocol that interfaces to potentially numerous varying computing platforms providing image processing and computer vision support. It provides a unified programming interface for the user despite the potential heterogeneity of the underlying hardware supporting the image processing. Computational platforms currently being studied include the Lockheed Martin PAL system, floating point gate arrays (FPGAs), and symmetric multiprocessors (SMPs). The Open distributed processing Reference Model (ODP-RM) is an ISO standards effort (ISO/IEC 10746-1/2/3/4) to address the specification and implementation of distributed processing systems, providing an architecture for integrating distribution support, interworking, and portability. The ODP Reference Model is exploited in the design to support transparent distribution of image processing and computer vision processes to available computational devices. The AIM System's ODP Model is specified using Object-Z, a formal descriptive notation that is commonly employed for ODP specification. Use of Object-Z notation supports formal analysis of the system's properties, helping us verify that its design satisfies its goals. This paper presents an example image processing algorithm (Simple image Statistic Thresholding) as a framework for understanding both the method of distribution and the benefits obtained from the use of our model description.

关键词： ODP image processing Object-Z formal descriptive techniques image algebra fault tolerance open distributed processing

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