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KarhunenLoève Transform: An Exercise in Simple ImageProcessing Parallel Pipelines
 In EuroPar'97
, 1997
"... . Practical parallelizations of multiphased lowlevel imageprocessing algorithms may require working in batch mode. The features of a common processing model, employing a pipeline of processor farms, are described. A simple exemplar, the KarhunenLo`eve transform, is prototyped on a network of ..."
Abstract

Cited by 5 (4 self)
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. Practical parallelizations of multiphased lowlevel imageprocessing algorithms may require working in batch mode. The features of a common processing model, employing a pipeline of processor farms, are described. A simple exemplar, the KarhunenLo`eve transform, is prototyped on a network of processors running a realtime operating system. The design tradeoffs for this and similar algorithms are indicated, when a general solution is sought. Eventual implementation on large and fine grained hardware is considered. The chosen exemplar is shown to have some features, such as strict sequencing and unbalanced processing phases, which militate against a comfortable parallelization. 1 Introduction Many lowlevel imageprocessing (IP) algorithms, such as spatial filters, are completely localized in their data references. If adjacent image data are overlapped at boundaries then at a small additional cost a datafarming programming paradigm can be employed, in which the only com...
Scheduling Schemes for Data Farming
 IEE Proceedings Part E (Computers and Digital Techniques
, 1999
"... The use of order statistics to arrive at a scheduling regime is shown to be applicable to data farms running on secondgeneration parallel processors. Uniform and decreasing tasksize scheduling regimes are examined. Experimental timings and a further simulation for largescale effects were used ..."
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Cited by 1 (1 self)
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The use of order statistics to arrive at a scheduling regime is shown to be applicable to data farms running on secondgeneration parallel processors. Uniform and decreasing tasksize scheduling regimes are examined. Experimental timings and a further simulation for largescale effects were used to exercise the scheduling regimes. The paper also considers a number of other scheduling schemes for data farms. It is shown that a method previously used for loopscheduling is preferable, particularly as a form of automatic and generalised scheduling for data farming where there is a datadependent workload. 1 Introduction A processor or data farm [ 1 ] is a programming paradigm involving messagepassing in which a single task is repeatedly executed in parallel on a collection of initial data. Datafarming is a commonlyused paradigm in parallel processing [ 2 ] and appears in numerous guises: some (networkofworkstations) NOWbased [ 3 ] ; some based on dedicated multicomputers [ 4...
EFFICIENT IMAGE RECONSTRUCTION USING PARTIAL 2D FOURIER TRANSFORM †
"... In this paper we present an efficient way of doing image reconstruction using the 2D Discrete Fourier transform (DFT). We exploit the fact that in the frequency domain, information is concentrated in certain regions. Consequently, it is sufficient to compute partial 2D Fourier transform where only m ..."
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In this paper we present an efficient way of doing image reconstruction using the 2D Discrete Fourier transform (DFT). We exploit the fact that in the frequency domain, information is concentrated in certain regions. Consequently, it is sufficient to compute partial 2D Fourier transform where only m ×m elements of an N ×N image are nonzero. Compared with the traditional rowcolumn (RC) decomposition algorithm, the proposed algorithm enables us to reconstruct images with significantly smaller computation complexity at the expense of mild degradation in quality. We also describe the implementation of the new reconstruction algorithm on a Xilinx VirtexII Pro100 FPGA. For 512 × 512 natural and aerial images, this implementation results in 68 % reduction in the number of memory accesses and 76 % reduction in the total computation time compared to the RC method.