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An Efficient Parallel SURF Algorithm for Multi-core Processor

  • Zhong Liu
  • Binchao Xing
  • Yueyue Chen
Part of the Communications in Computer and Information Science book series (CCIS, volume 337)

Abstract

In this paper, we propose an efficient parallel SURF algorithm for multi-core processor, which adopts data-level parallel method to implement parallel keypoints extraction and matching. The computing tasks are assigned to four DSP cores for parallel processing. The multi-core processor utilizes QLink and SDP respectively to deal with data communication and synchronization among DSP cores, which fully develops the multi-level parallelism and the strong computing power of multi-core processor. The parallel SURF algorithm is fully tested based on 5 different image samples with scale change, rotation, change in illumination, addition of noise and affine transformation The experimental results show that the parallel SURF algorithm has good adaptability for various distorted images, good image matching ability close to the sequential algorithm and the average speedup is 3.61.

Keywords

parallel SURF image matching multi-core processor 

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References

  1. 1.
    Todorovic, S., Ahuja, N.: Scale-invariant Region-based Hierarchical Image Matching. In: Proc. 19th International Conference on Pattern Recognition (ICPR), Tampa, FL (December 2008)Google Scholar
  2. 2.
    Toews, M., Wells III, W.M., Louis Collins, D., Arbel, T.: Feature-based Morphometry: Discovering Group-related Anatomical Patterns. NeuroImage 49(3), 2318–2327 (2010)CrossRefGoogle Scholar
  3. 3.
    Lowe, D.G.: Distinctive image features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  4. 4.
    Bay, H., Tuytelaars, T., van Gool, L.: Speeded-up Robust Features (SURF). Computer Vision and Image Understanding (2007)Google Scholar
  5. 5.
    Chen, S.M., Wan, J.H., Lu, J.Z., et al.: YHFT-QDSP: High-performance heterogeneous multi-core DSP. Journal of Computer Science and Technology 25(2), 214–224 (2010)CrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
  7. 7.
    Simard, P., Bottou, L., Haffner, P.: Boxlets: a fast convolution algorithm for signal processing and neural networks. In: Advances in Neural Information Processing Systems (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhong Liu
    • 1
  • Binchao Xing
    • 1
  • Yueyue Chen
    • 1
  1. 1.Microelectronics and Microprocessor Institute, School of ComputerNational University of Defense TechnologyChangshaChina

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