Fast spinning card recognition

Group Members:

  • David Mascarenas, Los Alamos National Labs
  • Daniel Lofaro, Drexel University
  • Ryad Benjamin Benosman, University Pierre and Marie Curie
  • Tobi Delbruck, Institute of Neuroinformatics, UZH/ETH Zurich

This workgroup will develop a fast numerical card recognizer while the card is spinning under its field of view.

Some sample data is available - download DVS128-2010-06-29T04-25-08%2B0200-0%20cards%20tilted.aedat or DVS128-2010-06-29T04-23-35%2B0200-0%20cards%20tilted.aedat. These two files are recorded from the  DVS silicon retina and can be viewed using jAER.

A screen shot of 512 events spanning 2ms of real time from the DVS is below.

screen shot jaer of spinning card

Fast DVS recognizer

David Mascarenas Tobi Delbruck

Fast DVS recognizer using FPAER

Marco Rodrigues

The goal of this project was to develop a hardware implemented fast French card recognizer. In this project we used a DVS 128 sensor (1), a pan and tilt unit (for shaking the DVS because it only “see” the motion), a FPAER that is stack of 4 USB-AER boards (2) (USB-AER board is AER board based on a FGPA) and a computer devoted to card classification.

The system works as follow: the DVS sends by a AER parallel cable events that correspond with the motion, these events go to the first USB-AER board that is configured as a AER monitor, then the events go to the next board configured as a background activity filter (this filter is similar to the jAER one (3) but described in VHDL); the third board is devoted to cluster tracking (4); The cluster tracking information is downloaded to the PC, that is running a classifier based on the relative position of the cluster (each cluster corresponds with the card's tips); the classifier returns the most probable card.

The cluster tracker only can track up to 8 objects so only card from As to 8s can be recognized. The performance of the system is shown in the next table.

A80 %100%
2100 %100 %
3100 %90 %
4100 %100 %
5100 %90 %
6100 %90 %
770 % 70 %
830 % 20 %

See video demo: https://neuromorphs.net/nm/attachment/wiki/2010/snapc10/FastCardPaco.wmv


(1) F. Gómez-Rodríguez, R. Paz, A. Linares-Barranco, M. Rivas, L. Miró, G. Jiménez, A. Civit. “AER tools for Communications and Debugging”. Proc. IEEE ISCAS06. Kos, Greece, May 2006.

(2) P. Lichtsteiner, et al., "A 128×128 120dB 30mW Asynchronous Vision Sensor that Responds to Relative Intensity Change," ISSCC Dig. of Tech. Papers, San Francisco, 2006, pp. 508-509 (27.9).

(3) jAER:  http://sourceforge.net/apps/trac/jaer/wiki

(4) F. Gómez- Rodríguez, L. Miró-Amarante, F. Diaz-del-Rio, A. Linares-Barranco, G. Jimenez. “Real time objects tracking using a bio-inspired processing cascade architecture” Proc. IEEE ISCAS10. Paris, France, May 2010.

Card recognizer using phase coding network

'Kazuki Nakada'

We use a Markov Random field model which uses phase dynamics to segment regions in the image follow the equations in Figure 1. Phase coding can link both rate and temporal coding schemes together. The simulation results in the figure show how different regions with the same phase dynamics emerge from the original image.

Figure 1. Multi-scale phase coding.

Sliding DVS recognizer

Daniel Lofaro