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Stream Processing of Geometric and Central Moments Using High Precision Summed Area Tables

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5506))

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Abstract

This paper introduces a stream programming based design of the zero and higher order central moments that use an integral image or summed area data structure of geometric moments. The stream programming algorithm runs on a general purpose graphics processing unit (GPGPU) that are becoming commodity hardware, giving real-time performance even for large image sizes and a large number of scan window sizes.

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References

  1. Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Transactions on Information Theory 8, 179–187 (1962)

    MATH  Google Scholar 

  2. Flusser, J.: On the Independence of Rotation Moment Invariants. Pattern Recognition 33, 1405–1410 (2000)

    Article  Google Scholar 

  3. Barczak, A.L., Johnson, M.J., Messom, C.H.: Revisiting Moment Invariants: Rapid Feature Extraction and Classification for Handwritten Digits. In: Proceedings of IVCNZ (2007)

    Google Scholar 

  4. Barczak, A.L.C., Dadgostar, F., Messom, C.H.: Real-Time Hand tracking based on non-invarient features. In: IEEE Instrumentation and Measurement Technology Conference, Ottawa, Canada, pp. 2192–2197 (2005); ISBN 0-7803-8879-8

    Google Scholar 

  5. Barreto, J., Menezes, P., Dias, J.: Human-robot interaction based on haar-like features and eigenfaces. In: International Conference on Robotics and Automation, New Orleans (2004)

    Google Scholar 

  6. Barczak, A.L.C.: Feature Based Rapid Object Detection: From Feature Extraction to Parallelisation, Phd Thesis, Massey University (2007)

    Google Scholar 

  7. Rowley, H., Baaluja, S., Kanade, T.: Neural Network Based Face Detection. IEEE Transactions on Patterrn Analysis and Machine Vision 20, 23–38 (1998)

    Article  Google Scholar 

  8. Jones, M., Viola, P.: Fast Multi-view Face Detection, Mitsubishi Electric Research Laborotories, TR2003-96 (2003)

    Google Scholar 

  9. Crow, F.C.: Summed-area tables for texture mapping. Computer Graphics 18, 207–212 (1984)

    Article  Google Scholar 

  10. Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., Hanrahan, P.: Brook for GPUs: Stream computing on graphics hardware. ACM Trans. Graph. 23(3), 777–786 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Information and Mathematical Sciences, Massey University, New Zealand

    Chris Messom & Andre Barczak

Authors
  1. Chris Messom
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  2. Andre Barczak
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Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Network Design and Research Center, 680-4 Fukuoka, 820-8502, Kawazu, Iizuka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street, 1142, Auckland, New Zealand

    George Coghill

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© 2009 Springer-Verlag Berlin Heidelberg

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Messom, C., Barczak, A. (2009). Stream Processing of Geometric and Central Moments Using High Precision Summed Area Tables. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_133

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  • DOI: https://doi.org/10.1007/978-3-642-02490-0_133

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02489-4

  • Online ISBN: 978-3-642-02490-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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