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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-7
Yashwanth Hemaraj , Department of Electrical and Computer Engineering, University of Maryland at College Park, USA
Mainak Sen , Department of Electrical and Computer Engineering, University of Maryland at College Park, USA
William Plishker , Department of Electrical and Computer Engineering, University of Maryland at College Park, USA
Raj Shekhar , Department of Electrical and Computer Engineering, University of Maryland at College Park, USA
Shuvra Bhattacharyya , Department of Electrical and Computer Engineering, University of Maryland at College Park, USA
ABSTRACT
This work targets the design of customized accelerators for image registration algorithms, which are required for many important computer vision applications. By capturing key, domain-specific characteristics of application structure, signal-processing-oriented models of computation provide a valuable foundation for structured development of efficient image registration accelerators. Building upon the meta-modeling framework of homogeneous parameterized dataflow, we develop in this paper an approach for automatically generating streamlined implementations of image registration algorithms according to performance metrics such as image size, area and overall processing speed. Results from hardware synthesis demonstrate the efficiency of our methods. Our approach provides designers an effective way to explore different architectures, and systematically provide acceleration for high-performance nonrigid image registration based on a variety of requirements. Our dataflow-based framework can be adapted to explore different architectures for other kinds of image processing algorithms as well.
CITATION
Yashwanth Hemaraj, Mainak Sen, William Plishker, Raj Shekhar, Shuvra Bhattacharyya, "Model-based mapping of a nonrigid image registration algorithm to heterogeneous architectures", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-7, doi:10.1109/CVPRW.2008.4563151
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