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Journal of Real-Time Image Processing

, Volume 5, Issue 4, pp 291–304 | Cite as

A two-level real-time vision machine combining coarse- and fine-grained parallelism

  • Lars Baunegaard With JensenEmail author
  • Anders Kjær-Nielsen
  • Karl Pauwels
  • Jeppe Barsøe Jessen
  • Marc Van Hulle
  • Norbert Krüger
Special Issue

Abstract

In this paper, we describe a real-time vision machine having a stereo camera as input generating visual information on two different levels of abstraction. The system provides visual low-level and mid-level information in terms of dense stereo and optical flow, egomotion, indicating areas with independently moving objects as well as a condensed geometric description of the scene. The system operates at more than 20 Hz using a hybrid architecture consisting of one dual-GPU card and one quad-core CPU. The different processing stages of visual information have rather different characteristics that in some cases make fine-grained parallelization on a GPU less applicable. However, for most of the stages that are not efficiently implementable on a GPU, a coarse parallelization on multiple CPU-cores is applicable. We show that with such hybrid parallelism, we can achieve a speed up of approximately a factor 90 and a reduction of latency of a factor 26 compared to processing on a single CPU-core. Since the vision machine provides generic visual information it can be used in many contexts. Currently it is used in a driver assistance context as well as in two robotic applications.

Keywords

Hybrid parallelism Early vision Mid-level vision GPU computing Multi-core 

Notes

Acknowledgments

This work was supported by the European Commission FP6 Project DRIVSCO (IST-016276-2) and the Danish project Robo-Packman.

References

  1. 1.
    Aarno, D., Sommerfeld, J., Kragic, D., Pugeault, N., Kalkan, S., Wörgötter, F., Kraft, D., Krüger, N.: Early reactive grasping with second order 3d feature relations. In: The IEEE International Conference on Advanced Robotics, Jeju Island, Korea (2007)Google Scholar
  2. 2.
    Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M.: Pyramid methods in image processing. RCA Eng. 29(6), 33–41 (1984)Google Scholar
  3. 3.
    Aloimonos, Y., Shulman, D.: Integration of Visual Modules—An Extension of the Marr Paradigm. Academic Press, London (1989)Google Scholar
  4. 4.
    Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities, pp. 79–81 (2000)Google Scholar
  5. 5.
    Başeski, E., Kraft, D., Krüger, N.: Road interpretation for driver assistance based on an early cognitive vision system. VISAPP (2009)Google Scholar
  6. 6.
    Baker, Z.K., Gokhale, M.B., Tripp, J.L.: Matched filter computation on FPGA, cell and GPU. Field-Programmable Custom Computing Machines. Annual IEEE Symposium, pp. 207–218 (2007). doi: 10.1109/FCCM.2007.52
  7. 7.
    Bouguet, J.Y.: Camera Calibration Toolbox for Matlab (2008). http://www.vision.caltech.edu/bouguetj/calib_doc/
  8. 8.
    Bugge. H.: An evaluation of Intel’s core i7 architecture using a comparative approach. Comput. Sci. Res. Dev. 23(3–4), 203–209 (2009) CrossRefGoogle Scholar
  9. 9.
    Che, S., Li, J., Sheaffer, J., Skadron, K., Lach, J.: Accelerating compute-Intensive applications with GPUs and FPGAs. In: Application Specific Processors, 2008. SASP 2008. Symposium, pp 101–107 (2008). doi: 10.1109/SASP.2008.4570793
  10. 10.
    Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by 2D visual cortical filters. J. Opt. Soc. Am. 2(7), 1160–1169 (1985) CrossRefGoogle Scholar
  11. 11.
    Detry, R., Pugeault, N., Piater, J.: A probabilistic framework for 3D visual object representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(10), 1790–1803 (2009) CrossRefGoogle Scholar
  12. 12.
    ECOVISION: Artificial visual systems based on early-cognitive cortical processing (EU-Project) (2001–2003). http://www.pspcdibeunigeit/ecovision/projecthtml
  13. 13.
    Felsberg, M., Sommer, G.: The monogenic signal. IEEE Trans. Signal Process. 49(12), 3136–3144 (2001)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Felsberg, M., Kalkan, S., Krüger, N.: Continuous dimensionality characterization of image structures. Image Vis. Comput. 27, 628–636 (2009) CrossRefGoogle Scholar
  15. 15.
    Fleet, D., Jepson, A.: Computation of component image velocity from local phase information. Int. J. Comput. Vis. 5, 77–104 (1990) CrossRefGoogle Scholar
  16. 16.
    Gautama, T., Van Hulle, M.: A phase-based approach to the estimation of the optical flow field using spatial filtering. IEEE Trans. Neural Netw. 13(5), 1127–1136 (2002) CrossRefGoogle Scholar
  17. 17.
    Granlund, G.: In search of a general picture processing operator. Comput. Graph. Image Process. 8, 155–173 (1978) CrossRefGoogle Scholar
  18. 18.
    Granlund, G.: The complexity of vision. Signal Process. 74 (1999)Google Scholar
  19. 19.
    Granlund, G.H., Knutsson, H.: Signal Processing for Computer Vision. Kluwer, Dordrecht (1995) Google Scholar
  20. 20.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)Google Scholar
  21. 21.
    Hubel, D., Wiesel, T.: Anatomical demonstration of columns in the monkey striate cortex. Nature 221, 747–750 (1969) CrossRefGoogle Scholar
  22. 22.
    Intel®: CoreTM i7 Desktop Processor, Product Brief (2009)Google Scholar
  23. 23.
    Jensen, L.B.W., Kjær-Nielsen, A., Alonso, J.D., Ros, E., Krüger, N.: A hybrid fpga/coarse parallel processing architecture for multi-modal visual feature descriptors. In: RECONFIG ’08: Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs, pp. 241–246 (2008)Google Scholar
  24. 24.
    Jessen, J.B.: Real time sparse and dense stereo in an early cognitive vision system using cuda. Master’s thesis. The Cognitive Vision Group, Maersk Institute, University of Southern Denmark (2009). http://www.mip.sdu.dk/covig/publications/JessenMaster.pdf
  25. 25.
    Kjær-Nielsen, A., Jensen, L.B.W., Sørensen, A.S., Krüger, N.: A real-time embedded system for stereo vision preprocessing using an fpga. In: RECONFIG ’08: Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs, pp. 37–42 (2008)Google Scholar
  26. 26.
    Kraft, D., Pugeault, N., Başeski, E., Popović, M., Kragic, D., Kalkan, S., Wörgötter, F., Krüger, N.: Birth of the Object: Detection of Objectness and Extraction of Object Shape through Object Action Complexes. Special Issue on “Cognitive Humanoid Robots” of the International Journal of Humanoid Robotics, vol. 5, pp. 247–265 (2009)Google Scholar
  27. 27.
    Krüger, N., Felsberg, M.: An explicit and compact coding of geometric and structural information applied to stereo matching. Pattern Recognit. Lett. 25(8), 849–863 (2004) CrossRefGoogle Scholar
  28. 28.
    Krüger, N., Lappe, M., Wörgötter, F.: (2004) Biologically motivated multi-modal processing of visual primitives. The interdisciplinary. J. Artifi. Intell. Simul. Behav. 1(5), 417–428Google Scholar
  29. 29.
    Lades, M., Vorbrüggen, J., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R., Konen, W.: Distortion invariant object recognition in the dynamik link architecture. IEEE Trans. Comput. 42(3), 300–311 (1993)CrossRefGoogle Scholar
  30. 30.
    Longuet-Higgins, H.C., Prazdny, K.: The interpretation of a moving retinal image. Proc. R. Soc. Lond. Ser. B Biol. Sci. 208, 385–397 (1980) CrossRefGoogle Scholar
  31. 31.
    Malvar, H., He L., Cutler, R.: High-quality linear interpolation for demosaicing of Bayer-patterned color images. In: Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP’04). IEEE International Conference on, vol 3, pp iii–485–8 (2004). doi: 10.1109/ICASSP.2004.1326587
  32. 32.
    Marr, D.: Vision. Freeman, San Francisco (1982)Google Scholar
  33. 33.
    Mosteller, F., Tukey, J.: Data Analysis and Regression: A Second Course in Statistics. Addison-Wesley Reading (1977) Google Scholar
  34. 34.
    Pauwels, K.: Computational modeling of visual attention: neuronal response modulation in the thalamocortical complex and saliency-based detection of independent motion. PhD thesis, K.U.Leuven (2008)Google Scholar
  35. 35.
    Pauwels, K., Van Hulle, M.: Realtime phase-based optical flow on the GPU. In: IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Computer Vision on the GPU (2008)Google Scholar
  36. 36.
    Pauwels, K., Krüger, N., Lappe, M., Wörgötter, F., Van Hulle, M.: A cortical architecture on parallel hardware for motion processing in real-time. J. Vis. (2010, submitted)Google Scholar
  37. 37.
    Pollen, D., Ronner, S.: Phase-relationships between adjacent simple cells in the visual cortex. Science 212(4501), 1409–1411 (1981)CrossRefGoogle Scholar
  38. 38.
    Pugeault, N.: Early Cognitive Vision: Feedback Mechanisms for the Disambiguation of Early Visual Representation. VDM Verlag Dr. Müller, Germany (2008)Google Scholar
  39. 39.
    Pugeault, N., Wörgötter, F., Krüger, N.: Accumulated visual representation for cognitive vision. In Proceedings of the British Machine Vision Conference (BMVC) (2008)Google Scholar
  40. 40.
    Sabatini, S., Gastaldi, G., Solari, F., Diaz, J., Ros, E., Pauwels, K., Van Hulle, M., Pugeault, N., Krüger, N.: Compact and accurate early vision processing in the harmonic space. In: International Conference on Computer Vision Theory and Applications, Barcelona, pp. 213–220 (2007)Google Scholar
  41. 41.
    Satish, N., Harris, M., Garland, M.: Designing efficient sorting algorithms for manycore GPUs. In: Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium, pp. 1–10 (2009). doi: 10.1109/IPDPS.2009.5161005
  42. 42.
    Schiele, B., Crowley, J.: Probabilistic object recognition using multidimensional receptive field histograms. Adv. Neural Info. Process. Syst. 8, 865–871 (1996) Google Scholar
  43. 43.
    Slama, C.C. (ed.): Manual of Photogrammetry. American Society of Photo (1980)Google Scholar
  44. 44.
    Thompson, W., Pong, T.: Detecting moving-objects. Int. J. Comput. Vis. 4, 39–57 (1990)CrossRefGoogle Scholar
  45. 45.
    Tröger, P.: The Multi-Core Era—Trends and Challenges. CoRR abs/0810.5439 (2008)Google Scholar
  46. 46.
    Vernon, D.: The space of cognitive vision. In: Cognitive Vision Systems, Part I: Foundations of Cognitive Vision Systems. LNCS, vol. 3948 (2006)Google Scholar
  47. 47.
    Wilkinson, B.: Computer Architecture (2nd ed.): Design and Performance. Prentice-Hall, Inc., Upper Saddle River (1996)Google Scholar
  48. 48.
    Wörgötter, F., Krüger, N., Pugeault, N., Calow, D., Lappe, M., Pauwels, K., Hulle, M.V., Tan, S., Johnston, A.: Early cognitive vision: Using gestalt-laws for task-dependent, active image-processing. Nat. Comput. 3(3), 293–321 (2004) zbMATHCrossRefMathSciNetGoogle Scholar
  49. 49.
    Zetzsche, C., Krieger, G.: Nonlinear mechanisms and higher-order statistics in biologial vision and electronic image processing: review and perspectives. J. Electron. Imaging 10(1), 56–99 (2001) CrossRefGoogle Scholar
  50. 50.
    Zhang, T., Tomasi, C.: On the consistency of instantaneous rigid motion estimation. Int. J. Comput. Vis. 46, 51–79 (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Lars Baunegaard With Jensen
    • 1
    Email author
  • Anders Kjær-Nielsen
    • 1
  • Karl Pauwels
    • 2
  • Jeppe Barsøe Jessen
    • 1
  • Marc Van Hulle
    • 2
  • Norbert Krüger
    • 1
  1. 1.The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkOdense MDenmark
  2. 2.Laboratorium voor Neuro- en PsychofysiologieK.U.LeuvenLeuvenBelgium

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