On the AER Stereo-Vision Processing: A Spike Approach to Epipolar Matching

  • Manuel Jesus Domínguez-Morales
  • Elena Cerezuela-Escudero
  • Fernando Perez-Peña
  • Angel Jimenez-Fernandez
  • Alejandro Linares-Barranco
  • Gabriel Jimenez-Moreno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)


Image processing in digital computer systems usually considers visual information as a sequence of frames. These frames are from cameras that capture reality for a short period of time. They are renewed and transmitted at a rate of 25-30 fps (typical real-time scenario). Digital video processing has to process each frame in order to detect a feature on the input. In stereo vision, existing algorithms use frames from two digital cameras and process them pixel by pixel until it finds a pattern match in a section of both stereo frames. To process stereo vision information, an image matching process is essential, but it needs very high computational cost. Moreover, as more information is processed, the more time spent by the matching algorithm, the more inefficient it is. Spike-based processing is a relatively new approach that implements processing by manipulating spikes one by one at the time they are transmitted, like a human brain. The mammal nervous system is able to solve much more complex problems, such as visual recognition by manipulating neuron’s spikes. The spike-based philosophy for visual information processing based on the neuro-inspired Address-Event- Representation (AER) is achieving nowadays very high performances. The aim of this work is to study the viability of a matching mechanism in a stereo-vision system, using AER codification. This kind of mechanism has not been done before to an AER system. To do that, epipolar geometry basis applied to AER system are studied, and several tests are run, using recorded data and a computer. The results and an average error are shown (error less than 2 pixels per point); and the viability is proved.


Address-Event-Representation spike neuromorphic engineering stereo epipolar geometry vision dynamic vision sensors retina 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barnard, S.T., Fischler, M.A.: Computational Stereo. Journal ACM CSUR 14(4) (1982)Google Scholar
  2. 2.
    Dominguez-Morales, M., et al.: An approach to distance estimation with stereo vision using Address-Event-Representation. In: International Conference on Neural Information Processing, ICONIP (2011)Google Scholar
  3. 3.
    Dominguez-Morales, M., et al.: Live Demonstration: on the Distance Estimation of Moving Targets with a Stereo-Vision Aer System. In: ISCASS (2012)Google Scholar
  4. 4.
    Shepherd, G.M.: The Synaptic Organization of the Brain, 3rd edn. Oxford University Press (1990)Google Scholar
  5. 5.
    Lee, J.: A Simple Speckle Smoothing Algorithm for Synthetic Aperture Radar Images. Man and Cybernetics SMC-13 (1981)Google Scholar
  6. 6.
    Crimmins, T.: Geometric Filter for Speckle Reduction. Applied Optics 24, 1438–1443 (1985)CrossRefGoogle Scholar
  7. 7.
    Linares-Barranco, A., et al.: AER Convolution Processors for FPGA. In: ISCASS (2010)Google Scholar
  8. 8.
    Sivilotti, M.: Wiring Considerations in analog VLSI Systems with Application to Field-Programmable Networks. Ph.D. Thesis, Caltech (1991)Google Scholar
  9. 9.
    Cope, B.: Implementation of 2D Convolution on FPGA, GPU and CPU. I.C. Report (2006)Google Scholar
  10. 10.
    Cope, B., et al.: Have GPUs made FPGAs redundant in the field of video processing? FPT (2005)Google Scholar
  11. 11.
    Lichtsteiner, P., Posh, C., Delbruck, T.: A 128×128 120dB 15 us Asynchronous Temporal Contrast Vision Sensor. IEEE Journal on Solid-State Circuits 43(2), 566–576 (2008)CrossRefGoogle Scholar
  12. 12.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004)Google Scholar
  13. 13.
    Jimenez-Fernandez, A., et al.: Building Blocks for Spike-based Signal Processing. In: IEEE International Joint Conference on Neural Networks, IJCNN (2010)Google Scholar
  14. 14.
    Rosenfeld, A.: First Textbook in Computer Vision: Picture Processing by Computer. Academic Press, New York (1969)Google Scholar
  15. 15.
    Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Int. Journal Robotics and Automation. 3(4), 323–344 (1987)CrossRefGoogle Scholar
  16. 16.
    Faugeras, O.: Three-dimensional computer vision: a geometric viewpoint. MIT Press (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Manuel Jesus Domínguez-Morales
    • 1
  • Elena Cerezuela-Escudero
    • 1
  • Fernando Perez-Peña
    • 2
  • Angel Jimenez-Fernandez
    • 1
  • Alejandro Linares-Barranco
    • 1
  • Gabriel Jimenez-Moreno
    • 1
  1. 1.Robotic and Technology of Computers LabUniversity of SevilleSpain
  2. 2.Applied Robotics Research LabUniversity of CadizSpain

Personalised recommendations