Skip to main content
SpringerLink
Log in

Speeding up the log-polar transform with inexpensive parallel hardware: graphics units and multi-core architectures

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Log-polar imaging is a kind of foveal, biologically inspired visual representation with advantageous properties in practical applications in computer vision, robotics, and other fields. While the cheapest, most flexible, and most common approach to get log-polar images is to use software-based mappers, this solution entails a cost which prevents certain experiments or applications from being feasible. This may be the case in some real-time (robotic) applications and, in general, when the conversion cost is not affordable for the task at hand. To overcome this drawback and make log-polar imaging more generally available, parallel solutions with affordable modern multi-core architectures have been devised, implemented, and tested in this work. Experimental results reveal that speed-up factors as high as or higher than 10 or 20, depending on the configuration, are possible to get log-polar images from large gray-level or color cartesian images using commodity graphics processors. Remarkable speedups are also reported for current multi-core processors. This noteworthy performance allows visual tasks that would otherwise be unthinkable with sequential implementations to become feasible. Additionally, since three different approaches have been explored and compared in terms of several criteria, different cost-effective choices are advisable depending on different visual task requirements or hardware availability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. CImg.: http://cimg.sourceforge.net. Accessed Sept 2012

  2. CUDA.: http://developer.nvidia.com/object/gpucomputing.html. Accessed Sept 2012

  3. OpenCL.: http://www.khronos.org/opencl. Accessed Sept 2012

  4. OpenGL.: http://www.opengl.org. Accessed Sept 2012

  5. OpenMP.: http://www.openmp.org. Accessed Sept 2012

  6. Visual Computer Vision on GPUs.: http://www.cs.unc.edu/~jmf/CV_GPU_program.html. CVPR 08 Workshop (2008). Accessed Sept 2012

  7. Babenko, P., Shah, M.: MinGPU: a minimum GPU library for computer vision. J. Real-Time Image Process. 3(4), 255–268 (2008)

    Article  Google Scholar 

  8. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. PAMI 24(4), 509–522 (2002). doi:10.1109/34.993558

    Google Scholar 

  9. Bernardino, A., Santos-Victor, J.: Visual behaviors for binocular tracking. Robot. Auton. Syst. 25, 137–146 (1998)

    Article  Google Scholar 

  10. Blake, G., Dreslinski, R., Mudge, T.: A survey of multicore processors. IEEE Signal Process. Mag. 26(6), 26–37 (2009)

    Article  Google Scholar 

  11. Blythe, D.: The Direct3D 10 system. ACM Trans. Graph 25(3), 724–734 (2006)

    Article  MathSciNet  Google Scholar 

  12. Bolduc, M., Levine, M.D.: A real-time foveated sensor with overlapping receptive fields. Real Time Imaging 3(3), 195–212 (1997)

    Article  Google Scholar 

  13. Böttger, J., Balzer, M., Deussen, O.: Complex logarithmic views for small details in large contexts. IEEE Trans. Visual. Comput. Graphics 12((5), 845–852 (2006)

    Article  Google Scholar 

  14. Bustos, P., Recio, F., Guinea, D., Garcia-Alegre, M.C.: Cortical representations in active vision on a network of transputers. In: ECPD International Conference on Advanced Robotics and Intelligent Automation, pp. 259–264, Athens, Greece (1995)

  15. Capurro, C., Panerai, F., Sandini, G.: Dynamic vergence using log-polar images. Int. J. Comput. Vis. 24(1), 79–94 (1997)

    Article  Google Scholar 

  16. Chen, T., Budnikov, D., Hughes, C., Chen, Y.K.: Computer vision on multi-core processors: articulated body tracking. In: IEEE International Conference on Multimedia and Expo, 2007, pp. 1862–1865 (2007)

  17. Chen, Y., Chakrabarti, C., Bhattacharyya, S., Bougard, B.: Signal processing on platforms with multiple cores. Part 2—applications and design [from the guest editors]. IEEE Signal Process. Mag. 27(2), 20–21 (2010)

    Article  Google Scholar 

  18. Chen, Y.K., Chakrabarti, C., Bhattacharyya, S., Bougard, B.: Signal processing on platforms with multiple cores. Part 1—overview and methodologies [from the guest editors]. IEEE Signal Process. Mag. 26(6), 24–25 (2009)

    Article  Google Scholar 

  19. CVGPU—Computer Vision on GPUs.: http://www.cvgpu.org. ECCV 10 Workshop, Crete, Greece (2010)

  20. El-Mahdy, A., El-Shishiny, H.: An efficient load-balancing algorithm for image processing applications on multicore processors. In: Proceedings of the 1st international forum on Next-generation multicore/manycore technologies, IFMT’08, pp. 8:1–8:5. ACM, New York (2008)

  21. Fisher, T.E., Juday, R.D.: A programmable video image remapper. In: SPIE Conference on Pattern Recognition and Signal Processing, vol. 938 (Digital and Optical Shape Representation and Pattern Recognition), pp. 122–128 (1988)

  22. Franchetti, F., Püschel, M., Voronenko, Y., Chellappa, S., Moura, J.M.F.: Discrete Fourier transform on multicore. IEEE Signal Process. Mag. 26(6), 90–102 (2009)

    Article  Google Scholar 

  23. Group, K.: OpenGL shading language (GLSL), v. 4.1. http://www.opengl.org/documentation/glsl (2010)

  24. Hartley, T.D.R., Ç atalyü rek, Ü.V., Ruiz, A., Igual, F.D., Mayo, R., Ujaldon, M.: Biomedical image analysis on a cooperative cluster of GPU s and multicores. In: ACM International Conference on Supercomputing (ICS), pp. 15–25 (2008)

  25. Hoan, L., Youngjae, C., Kyoungsu, O.: Image mosaic using log-polar binning. In: First Asian Conference on Pattern Recognition (ACPR), pp. 144–148 (2011)

  26. Jurie, F.: A new log-polar mapping for space variant imaging. Application to face detection and tracking. Pattern Recognit. 32, 865–875 (1999)

    Article  Google Scholar 

  27. Kim, D., Lee, V., Chen, Y.K.: Image processing on multicore x86 architectures. IEEE Signal Process. Mag. 27(2), 97–107 (2010)

    Article  Google Scholar 

  28. Kim, H., Bond, R.: Multicore software technologies. IEEE Signal Process. Mag. 26(6), 80–89 (2009)

    Article  Google Scholar 

  29. Massone, L., Sandini, G., Tagliasco, V.: ‘Form-Invariant’ topological mapping strategy for 2D shape recognition. Comput. Vis. Graph. Image Process. 30, 169–188 (1985)

    Article  Google Scholar 

  30. NVIDIA.: CUDA best practices guide (version 3.0). http://developer.download.nvidia.com/compute/cuda/3_0/toolkit/docs/NVIDIA_CUDA_BestPracticesGuide.pdf (2010)

  31. Pardo, F., Dierickx, B., Scheffer, D.: Space-variant nonorthogonal structure CMOS image sensor design. J. Solid-State Circ. 33(6), 842–849 (1998)

    Article  Google Scholar 

  32. Peng, F., Guo, R.S., Li, C.T., Long, M.: A semi-fragile watermarking algorithm for authenticating 2D CAD engineering graphics based on log-polar transformation. Comput.-Aid. Des. 42(12), 1207–1216 (2010)

    Article  Google Scholar 

  33. Poli, G., Saito, J.H., Mari, J.a.F., Zorzan, M.R.: Processing neocognitron of face recognition on high performance environment based on GPU with CUDA architecture. In: Proceedings of the 20th International Symposium on Computer Architecture and High Performance Computing, pp. 81–88 (2008)

  34. Powell, K.: Biomedical imaging ecosystem and the role of the GPU . In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI’09) , pp. 1291–1292 (2009)

  35. Rofouei, M., Stathopoulos, T., Ryffel, S., Kaiser, W., Sarrafzadeh, M.: Energy-aware high performance computing with graphic processing units. In: Proceedings of the 2008 conference on Power aware computing and systems (HotPower’08) (2008)

  36. Saini, N., Sinha, A.: Optics based biometric encryption using log polar transform. Opt. Commun. 283(1), 34–43 (2010)

    Article  Google Scholar 

  37. Samsi, S., Gadepally, V., Krishnamurthy, A.: MATLAB for signal processing on multiprocessors and multicores. IEEE Signal Process. Mag. 27(2), 40–49 (2010)

    Article  Google Scholar 

  38. Sandini, G., Dario, P., DeMicheli, M., Tistarelli, M.: Retina-like CCD sensor for active vision. In: Computer and Systems Sciences (NATO ARW on Robots and Biological Systems). Springer, Il Ciocco, Tuscany (1989)

  39. Schwartz, E.L., Greve, D.N., Bonmassar, G.: Space-variant active vision: definition, overview and examples. Neural Networks 8(7–8), 1297–1308 (1995)

    Article  Google Scholar 

  40. Setoain, J., Prieto, M., Tenllado, C., Tirado, F.: GPU for parallel on-board hyperspectral image processing. Int. J. High Perform. Comput. Appl 22, 424–437 (2008)

    Article  Google Scholar 

  41. Shin, C.W., Inokuchi, S., Kim, K.I.: Retina-like visual sensor for fast tracking and navigation robots. Mach. Vis. Appl. 10, 1–8 (1997)

    Article  Google Scholar 

  42. Slabaugh, G., Boyes, R., Yang, X.: Multicore image processing with OpenMP. IEEE Signal Process. Mag. 27(2), 134–138 (2010)

    Article  Google Scholar 

  43. del Solar, J.R., Nowack, C., Schneider, B.: VIPOL: a virtual polar-logarithmic sensor. In: Scandinavian Conference on Image Analysis (SCIA), pp. 739–744, Finland (1997)

  44. Traver, V.J., Bernardino, A.: A review of log-polar imaging for visual perception in robotics. Robot. Auton. Syst. 58(4), 378–398 (2010)

    Article  Google Scholar 

  45. Traver, V.J., Pla, F.: Similarity motion estimation and active tracking through spatial-domain projections on log-polar images. Comput. Vis. Image Underst. 97(2), 209–241 (2005)

    Article  Google Scholar 

  46. Traver, V.J., Pla, F.: Log-polar mapping template design: from task-level requirements to geometry parameters. Image Vis. Comput. 26(10), 1354–1370 (2008)

    Article  Google Scholar 

  47. Wallace, R.S., Ong, P.W., Bederson, B.B., Schwartz, E.L.: Space variant image processing. Int. J. Comput. Vis. 13(1), 71–90 (1994)

    Article  Google Scholar 

  48. Wang, Y.C., Donyanavard, B., Cheng, K.T.: Energy-aware real-time face recognition system on mobile CPU-GPU platform. In: http://www.cvgpu.org. ECCV 10 Workshop, Crete, Greece (2010)

  49. Weiman, C.F.R.: Video compression via log-polar mapping. In: SPIE Symposium on OE/Aerospace Sensing, Orlando, Florida (1990)

  50. Wilson, J.C., Hodgson, R.M.: Log-polar mapping applied to pattern representation and recognition. In: Computer Vision and Image Processing, pp. 245–277 (1992)

  51. Woelders, W.W., Frowein, H.W., Nielsen, J., Questa, P., Sandini, G.: New developments in low-bit rate videotelephony for people who are deaf. J. Speech Lang. Hearing Res. 40, 1425–1433 (1997)

    Article  Google Scholar 

  52. Wong, W.K., Choo, C.W., Loo, C.K., Teh, J.: FPGA implementation of log-polar mapping. Int. J. Comput. Appl. Technol. 39(1/2/3), 12–18 (2010)

    Article  Google Scholar 

  53. Wong, W.K., Choo, C.W., Loo, C.K., Teh, J.P.: FPGA implementation of log-polar mapping. In: 15th International Conference on Mechatronics and Machine Vision in Practice (M2VIP08), Auckland, New Zealand (2008)

  54. Wong, W.K., Loo, C.K., Lim, W.S., Tan, P.N.: Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification. Neurocomputing 74(1–3), 164–177 (2010)

    Article  Google Scholar 

  55. Zhang, X., Tay, L.P.: A spatial variant approach for vergence control in complex scenes. Image Vis. Comput. 29(1), 64–77 (2011)

    Article  Google Scholar 

  56. Zokai, S., Wolberg, G.: Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations. IEEE Trans. Image Process. 14(10), 1422–1434 (2005)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors acknowledge the funding from the Spanish research programme Consolider Ingenio-2010 CSD2007-00018, from Fundació Caixa-Castelló Bancaixa under project P1-1A2010-11, from project DPI-2008-06636 and FPI grant BES-2009-027151 from the Spanish Ministerio de Ciencia e Innovación, and the research fellowship PREDOC/2006/02 from Jaume I University.

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Jaume I University, Castellón, Spain

    Marco Antonelli

  2. Robotics Intelligence Laboratory, Jaume I University, Castellón, Spain

    Marco Antonelli

  3. Dept. Arquitectura de Computadores y Automática, Universidad Complutense de Madrid, Madrid, Spain

    Francisco D. Igual

  4. Computer Languages and Systems Department, Jaume I University, Castellón, Spain

    Francisco Ramos & V. Javier Traver

  5. Computer Graphics Group, Jaume I University, Castellón, Spain

    Francisco Ramos

  6. Computer Vision Group, Jaume I University, Castellón, Spain

    V. Javier Traver

  7. Institute of New Imaging Technologies (iNIT), Jaume I University, Castellón, Spain

    Francisco Ramos & V. Javier Traver

Authors
  1. Marco Antonelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco D. Igual
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Ramos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. Javier Traver
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisco D. Igual.

Additional information

Authors listed in alphabetical order due to similar contributions.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Antonelli, M., Igual, F.D., Ramos, F. et al. Speeding up the log-polar transform with inexpensive parallel hardware: graphics units and multi-core architectures. J Real-Time Image Proc 10, 533–550 (2015). https://doi.org/10.1007/s11554-012-0281-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-012-0281-6

Keywords

Search

Navigation