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Object oriented framework for real-time image processing on GPU

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Abstract

General purpose computation on graphics processing unit (GPGPU) provides a significant gain in terms of the processing time compared with CPU. Images are particularly good subjects for massive parallel implementations on GPU. Thus, the processing time can be improved for computer vision and image/video processing algorithms. However, GPGPU has a fairly complex integration process in a framework and they evolve very rapidly. In this paper, we present a framework that provides all the desired primitives related to GPGPU-based image processing algorithms, which makes it easy and straightforward for the user to exploit. The proposed framework is object-oriented, and it utilizes design patterns. The user can benefit from all the advantages of object-oriented programming, such as code reusability/extensibility, flexibility, information hiding, and complexity hiding. This makes it possible to rapidly integrate new technologies and functionality as they appear.

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Acknowledgements

This work was supported by the Industrial Strategic Technology Development Program (10041664, The Development of Fusion Processor based on Multi-Shader GPU) funded by the Ministry of Knowledge Economy (MKE, Korea).

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Authors and Affiliations

  1. SmartCo, Paris, 75008, France

    Nicolas Seiller

  2. Biomedical Signal Analysis Lab., GE Global Research, Bangalore, 560066, India

    Nitin Singhal

  3. School of Information and Communication Engineering, Inha University, Incheon, 402-751, Korea

    Williem & In Kyu Park

Authors
  1. Nicolas Seiller
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  2. Williem
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  3. Nitin Singhal
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  4. In Kyu Park
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Corresponding author

Correspondence to In Kyu Park.

Appendix: Algorithm listings

Appendix: Algorithm listings

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Seiller, N., Williem, Singhal, N. et al. Object oriented framework for real-time image processing on GPU. Multimed Tools Appl 70, 2347–2368 (2014). https://doi.org/10.1007/s11042-013-1440-x

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