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Multi-CPU/Multi-GPU Based Framework for Multimedia Processing

  • Conference paper

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 456)

Abstract

Image and video processing algorithms present a necessary tool for various domains related to computer vision such as medical applications, pattern recognition and real time video processing methods. The performance of these algorithms have been severely hampered by their high intensive computation since the new video standards, especially those in high definitions require more resources and memory to achieve their computations. In this paper, we propose a new framework for multimedia (single image, multiple images, multiple videos, video in real time) processing that exploits the full computing power of heterogeneous machines. This framework enables to select firstly the computing units (CPU or/and GPU) for processing, and secondly the methods to be applied depending on the type of media to process and the algorithm complexity. The framework exploits efficient scheduling strategies, and allows to reduce significantly data transfer times thanks to an efficient management of GPU memories and to the overlapping of data copies by kernels executions. Otherwise, the framework includes several GPU-based image and video primitive functions, such as silhouette extraction, corners detection, contours extraction, sparse and dense optical flow estimation. These primitives are exploited in different applications such as vertebra segmentation in X-ray and MR images, videos indexation, event detection and localization in multi-user scenarios. Experimental results have been obtained by applying the framework on different computer vision methods showing a global speedup ranging from 5 to 100, by comparison with sequential CPU implementations.

Keywords

  • GPU
  • Heterogeneous architectures
  • Image and video processing
  • Medical imaging
  • Motion tracking

References

  1. Augonnet, C., Thibault, S., Namyst, R., Wacrenier, P.-A.: StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 863–874. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  2. Bouguet, J.Y.: Pyramidal Implementation of the Lucas Kanade Feature Tracker, Description of the algorithm. Intel Corporation Microprocessor Research Labs, 851–862 (2000)

    Google Scholar 

  3. Deriche, R., Blaszka, T.: Recovering and characterizing im-age features using an efficient model based approach. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, New York, USA, pp. 530–535 (1993)

    Google Scholar 

  4. Grama, A., Gupta, A., Karypis, G., Kumar, V.: Introduction to Parallel Computing, 2nd edn. Pearson Education Limited (2003)

    Google Scholar 

  5. Harris, C.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–152 (1988)

    Google Scholar 

  6. Horn, B.K.P., Schunk, B.G.: Determining Optical Flow. Artificial Intelligence 2, 185–203 (1981)

    CrossRef  Google Scholar 

  7. Larhmam, M.A., et al.: A portable multi-cpu/multi-gpu based vertebra localization in sagittal mr images. In: International Conference on Image Analysis and Recognition, ICIAR 2014, pp. 209–218 (2014)

    Google Scholar 

  8. Lecron, F., et al.: Heterogeneous computing for vertebra detection and segmentation in x-ray images. International Journal of Biomedical Imaging: Parallel Computation in Medical Imaging Applications 2011, 1–12 (2011)

    CrossRef  Google Scholar 

  9. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV) 60(2), 91–110 (2004)

    CrossRef  Google Scholar 

  10. Mahmoudi, S.A., et al.: Multi-gpu based event detection and localization using high definition videos. In: International Conference on Multimedia Computing and Systems (ICMCS), pp. 81–86 (2014)

    Google Scholar 

  11. Mahmoudi, S.A., Kierzynka, M., Manneback, P., Kurowski, K.: Real-time motion tracking using optical flow on multiple gpus. Bulletin of the Polish Academy of Sciences: Technical Sciences 62, 139–150 (2014)

    CrossRef  Google Scholar 

  12. Mahmoudi, S.A., Lecron, F., Manneback, P., Benjelloun, M., Mahmoudi, S.: GPU-Based Segmentation of Cervical Vertebra in X-Ray Images. In: IEEE International Conference on Cluster Computing HPCCE Workshop, pp. 1–8 (2010)

    Google Scholar 

  13. Mahmoudi, S.A., Manneback, P.: Efficient exploitation of heterogeneous platforms for images features extraction. In: 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 91–96 (2012)

    Google Scholar 

  14. Marzat, J., Dumortier, Y., Ducrot, A.: Real-time dense and accurate parallel optical flow using CUDA. In: Proceedings of WSCG, pp. 105–111 (2009)

    Google Scholar 

  15. Park, K., Nitin, S., Man, H.L.: Design and Performance Evaluation of Image Processing Algorithms on GPUs. IEEE Transactions on Parallel and Distributed Systems 28, 1–14 (2011)

    Google Scholar 

  16. Ricardo Possa, P., Mahmoudi, S.A., Harb, N., Valderrama, C., Manneback, P.: A multi-resolution fpga-based architecture for real-time edge and corner detection. IEEE Transactions on Computers 63, 2376–2388 (2014)

    CrossRef  Google Scholar 

  17. Sinha, S.N., Fram, J.-M., Pollefeys, M., Genc, Y.: Gpu-based video feature tracking and matching. In: EDGE, Workshop on Edge Computing Using New Commodity Architectures (2006)

    Google Scholar 

  18. Tardieu, D., al.: Video navigation tool: Application to browsing a database of dancers’ performances. In: QPSR of the numediart research program, vol. 2(3), pp. 85–90 (2009)

    Google Scholar 

  19. Yang, Z., Zhu, Y., Pu, Y.: Parallel Image Processing Based on CUDA. In: International Conference on Computer Science and Software Engineering China, pp. 198–201 (2008)

    Google Scholar 

  20. Zhu, S., Ma, K.-K.: A new diamond search algorithm for fast block-matching motion estimation. IEEE Transactions on Image Processing 9(2), 287–290 (2000)

    CrossRef  MathSciNet  Google Scholar 

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Correspondence to Sidi Ahmed Mahmoudi .

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© 2015 IFIP International Federation for Information Processing

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Mahmoudi, S.A., Manneback, P. (2015). Multi-CPU/Multi-GPU Based Framework for Multimedia Processing. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-19578-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-19578-0_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19577-3

  • Online ISBN: 978-3-319-19578-0

  • eBook Packages: Computer ScienceComputer Science (R0)