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GPU-Based Real-Time Range Image Segmentation

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Intelligent Computing Methodologies (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8589))

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Abstract

In this paper proposed a GPU-based parallel processing method for real-time image segmentation with neural oscillator network. Range image segmentation methods can be divided into two categories: edge-based and region-based. Edge-base method is sensitive to noise and region-based method is hard to extracting the boundary detail between the object. However, by using LEGION (Locally Excitatory Globally Inhibitory oscillator networks) to do range image segmentation can overcome above disadvantages. The reason why LEGION is suitable for parallel processing that each oscillator calculate with its 8-neiborhood oscillators in real time when we process image segmentation by LEGION. Thus, using GPU-based parallel processing with LEGION can improve the speed to realize real-time image segmentation.

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References

  1. Liu, X., Wang, D.L.: Range Image Segmentation Using An Oscillatory Network. IEEE Trans. Neural Networks 10, 564–573 (1999)

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  2. Terman And, D., Wang, D.L.: Global Competition And Local Cooperation in A Network of Neural Oscillators. Physicu D 82, 148–176 (1995)

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  3. Wang, D.L., Terman, D.: Image Segmentation Based On Oscillatory Correlation. Neural Computation (in Press); See Also Technical Report 19, Center for Cognitive Science. The Ohio State University (1996)

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  4. CUDA C PROGRAMMING GUIDE http://Docs.Nvidia.Com/Cuda/Cuda-C-Programming-Guide/Index.Html

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Author information

Authors and Affiliations

  1. Robot Vision, Intelligence Lab Kwangwoon University, Seoul, Korea

    Xinhua Jin & Mun-Ho Jeong

  2. Mechanical Engineering, Pusan National University, Seoul, Korea

    Dong Joong Kang

Authors
  1. Xinhua Jin
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  2. Dong Joong Kang
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  3. Mun-Ho Jeong
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, 680–749, Ulsan, Korea

    Kang-Hyun Jo

  3. Department of Automation, Tsinghua University, Bejing, China

    Ling Wang

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© 2014 Springer International Publishing Switzerland

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Jin, X., Kang, D.J., Jeong, MH. (2014). GPU-Based Real-Time Range Image Segmentation. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_30

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  • DOI: https://doi.org/10.1007/978-3-319-09339-0_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09338-3

  • Online ISBN: 978-3-319-09339-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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