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Green Image
Issue No. 06 - June (2012 vol. 18)
ISSN: 1077-2626
pp: 838-851
C. T. Silva , Six Metrotech Center, Polytech. Inst. of NYU, Brooklyn, NY, USA
J. L. D. Comba , Inst. de Inf., Fed. Univ. of Rio Grande do Sul, Porto Alegre, Brazil
J. Kruger , Univ. of Saarland, Saarbrucken, Germany
L. K. Ha , Sci. Imaging & Comput. Inst., Univ. of Utah, Salt Lake City, UT, USA
S. Joshi , Sci. Imaging & Comput. Inst., Univ. of Utah, Salt Lake City, UT, USA
Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.
statistical analysis, graphics processing units, image processing, parallel processing, pipeline processing, out-of-core approach pipeline processing, ISP framework, optimal out-of-core image-set processing streaming architecture, parallel heterogeneous systems, image population analysis, statistical methods, supercomputing power, CPU-GPU systems, streaming algorithms, data structures, memory usage, computational efficiency, asynchronous execution mechanism, Streaming media, Graphics processing unit, MIMO, Hardware, Computational modeling, Data models, Parallel processing, multiimage processing framework., GPUs, out-of-core processing, atlas construction, diffeomorphism

C. T. Silva, J. L. Comba, J. Kruger, L. K. Ha and S. Joshi, "ISP: An Optimal Out-of-Core Image-Set Processing Streaming Architecture for Parallel Heterogeneous Systems," in IEEE Transactions on Visualization & Computer Graphics, vol. 18, no. , pp. 838-851, 2012.
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