Selective change driven imaging: A biomimetic visual sensing strategy

Sensors

Volumn 11, Issue 11, 2011, Pages 11000-11020

  Boluda, Jose A ^a   Zuccarello, Pedro ^a   Pardo, Fernando ^a   Vegara, Francisco ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Biomimetics; Cmos image sensor; Event based vision; Motion analysis

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOMIMETICS; COMPUTER SIMULATION; IMAGE PROCESSING; METHODOLOGY; MOTION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMIMETICS; COMPUTER SIMULATION; IMAGE PROCESSING, COMPUTER-ASSISTED; MOTION;

EID: 82055185557     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s111111000     Document Type: Article

References (58)

1. Stroble, J.K.; Stone, R.B.; Watkins, S.E. An overview of biomimetic sensor technology. Sensor Rev. 2009, 29, 112-119.
2. Schmitt, O.H. Some Interesting and Useful Biomimetic Transforms. In Proceedings of the Third International Biophysics Congress, Boston, MA, USA, 29 August-3 September 1969; p. 297.
3. Son, D.V.T.; Etoh, T.G.; Tanaka, M.; Dung, N.H.; Le Cuong, V.; Takehara, K.; Akino, T.; Nishi, K.; Aoki, H.; Nakai, J. Toward 100 Mega-Frames per second: Design of an ultimate ultra-high-speed image sensor. Sensors 2010, 10, 16-35.
4. Taghibakhsh, F.; Karim, K.S. Two-transistor active pixel sensor readout circuits in amorphous silicon technology for high-resolution digital Imaging applications. IEEE Trans. Electron. Dev. 2008, 55, 2121-2128.
5. El-Desouki, M.; Deen, M.J.; Fang, Q.; Liu, L.; Tse, F.; Armstrong, D. CMOS image sensors for high speed applications. Sensors 2009, 9, 430-444.
6. Etoh, T.; Poggemann, D.; Kreider, G.; Mutoh, H.; Theuwissen, A.; Ruckelshausen, A.; Kondo, Y.; Maruno, H.; Takubo, K.; Soya, H.; Takehara, K.; Okinaka, T.; Takano, Y. An image sensor which captures 100 consecutive frames at 1,000,000 frames/s. IEEE Trans. Electron. Dev. 2003, 50, 144-151.
7. Boyle, W.S.; Smith, G.E. Charge-coupled semiconductor devices. AT&T Tech. J. 1970, 49, 587-593.
8. Gollisch, T.; Meister, M. Eye smarter than scientists believed: Neural computations in circuits of the Retina. Neuron 2010, 65, 150-164.
9. Gollisch, T.; Meister, M. Rapid neural coding in the retina with relative spike latencies. Science 2008, 319, 1108-1111.
10. Sezan, M.I.; L. L.R. Motion Analysis and Image Sequence Processing; The International Series in Engineering and Computer Science, Springer-Verlag New York, Inc: Secaucus, NJ, USA, 1993.
11. Dowling, J.E. The Retina: An Approachable Part of the Brain; Belknap Press of Harvard University Press: London, UK, 1987.
12. Traver, V.J.; Bernardino, A. A review of log-polar imaging for visual perception in robotics. Robot Auton. Syst. 2010, 58, 378-398.
13. Traver, V.J.; Pla, F. Log-polar mapping template design: From task-level requirements to geometry parameters. Image Vision Comput. 2008, 26, 1354-1370.
14. Pardo, F.; Dierickx, B.; Scheffer, D. Space-variant nonorthogonal structure CMOS image sensor design. IEEE J. Solid-St. Circ. 1998, 33, 842-849.
15. Boluda, J.A.; Domingo, J. On the advantages of combining differential algorithms and log-polar vision for detection of self-motion from a mobile robot. Robot Auton. Syst. 2001, 37, 283-296.
16. de Croon, G.; Sprinkhuizen-Kuyper, I.; Postma, E. Comparing active vision models. Image Vision Comput. 2009, 27, 374-384.
17. Moini, A. Vision Chips; Kluwer Academic: Boston, UK, 2000; p. 300.
18. Guzman, P.; Diaz, J.; Agis, R.; Ros, E. Optical flow in a smart sensor based on hybrid analog-digital architecture. Sensors 2010, 10, 2975-2994.
19. Bigas, M.; Cabruja, E.; Forest, J.; Salvi, J. Review of CMOS image sensors. Microelectr. J. 2006, 37, 433-451.
20. Boahen, K. Neuromorphic microchips. Sci. Am. 2005, 292, 56-63.
21. Indiveri, G.; Douglas, R. Robotic vision-Neuromorphic vision sensors. Science 2000, 288, 1189-1190.
22. Etienne-Cummings, R.; VanderSpiegel, J. Neuromorphic vision sensors. Sensors Actuat. A Phys. 1996, 56, 19-29.
23. Mahowald, M. VLSI Analogs of Neural Visual Processing: A Synthesis of Form and Function. PhD Thesis, Computer Science Divivision, California Institute of Technology, Pasadena, CA, USA, 1992.
24. Boahen, K. A burst-mode word-serial address-event link-I: Transmitter design. IEEE Trans. Circuit. Syst. 2004, 51, 1269-1280.
25. Boahen, K. A burst-mode word-serial address-event link-II: Receiver design. IEEE Trans. Circuit. Syst. 2004, 51, 1281-1291.
26. Delbruck, T.; Linares-Barranco, B.; Culurciello, E.; Posch, C. Activity-Driven, Event-Based VisionSensors. In Proceedings of 2010 IEEE International Symposium on Circuits and Systems, Paris, France, 30 May-02 June 2010.
27. Lichtsteiner, P.; Posch, C.; Delbruck, T. A 128x128 120 dB 15 s latency asynchronous temporal contrast vision sensor. IEEE J. Solid-St. Circ. 2008, 43, 566-576.
28. Posch, C.; Matolin, D.; Wohlgenannt, R. A QVGA 143 dB dynamic range frame-rree PWM image sensor with lossless pixel-level video compression and time-domain CDS. IEEE J. Solid-St. Circ. 2011, 46, 259-275.
29. Indiveri, G. Neuromorphic analog VLSI sensor for visual tracking: Circuits and application examples. IEEE Trans. Circuit. Syst. 1999, 46, 1337-1347.
30. Higgins, C.; Koch, C. A modular multi-chip neuromorphic architecture for real-time visual motion processing. Analog Integr. Circuit. Signal 2000, 24, 195-211.
31. Kameda, S.; Yagi, T. A Silicon Retina System that Calculates Direction of Motion. In Proceedings of the 2003 IEEE International Symposium on Circuits and Systems, ISCAS'03, Bangkok, Thailand, 25-28 May 2003; Volume IV, pp. 792-795.
32. Culurciello, E.; Etienne-Cummings, R.; Boahen, K. A biomorphic digital image sensor. IEEE J. Solid-St. Circ. 2003, 38, 281-294.
33. Gottardi, M.; Massari, N.; Jawed, S.A. A 100 W 128 × 64 pixels contrast-based asynchronous binary vision sensor for sensor networks applications. IEEE J. Solid-St. Circ. 2009, 44, 1582-1592.
34. Indiveri, G. A neuromorphic VLSI device for implementing 2-D selective attention systems. IEEE Trans. Neural Networ. 2001, 12, 1455-1463.
35. Indiveri, G.; Murer, R.; Kramer, J. Active vision using an analog VLSI model of selective attention. IEEE Trans. Circuit. Syst. 2001, 48, 492-500.
36. Bartolozzi, C.; Indiveri, G. Selective attention in multi-chip address-event systems. Sensors 2009, 9, 5076-5098.
37. Liu, S.C.; Viretta, A.U. Fly-like visuomotor responses of a robot using aVLSI motion-sensitive chips. Biol. Cybern. 2001, 85, 449-457.
38. Higgins, C.; Pant, V. A biomimetic VLSI sensor for visual tracking of small moving targets. IEEE Trans. Circuit. Syst. 2004, 51, 2384-2394.
39. Ozalevli, E.; Higgins, C. Reconfigurable biologically inspired visual motion systems using modular neuromorphic VLSI chips. IEEE Trans. Circuit. Syst. 2005, 52, 79-92.
40. R̈uedi, P.; Heim, P.; Kaess, F.; Grenet, E.; Heitger, F.; Burgi, P.; Gyger, S.; Nussbaum, P. A 128 × 128, pixel 120-dB dynamic-range vision-sensor chip for image contrast and orientation extraction. IEEE J Solid-St. Circ. 2003, 38, 2325-2333.
41. Chicca, E.; Whatley, A.M.; Lichtsteiner, P.; Dante, V.; Delbruck, T.; Del Giudice, P.; Douglas, R.J.; Indiveri, G. A multichip pulse-based neuromorphic infrastructure and its application to a model of orientation selectivity. IEEE Trans. Circuit. Syst. 2007, 54, 981-993.
42. Camunas-Mesa, L.; Acosta-Jimenez, A.; Zamarreno-Ramos, C.; Serrano-Gotarredona, T.; Linares-Barranco, B. A 32 × 32 pixel convolution processor chip for address event vision sensors with 155 ns event latency and 20 Meps throughput. IEEE Trans. Circuit. Syst. 2011, 58, 777-790.
43. Serrano-Gotarredona, R.; Oster, M.; Lichtsteiner, P.; Linares-Barranco, A.; Paz-Vicente, R.; Gomez-Rodriguez, F.; Camunas-Mesa, L.; Berner, R.; Rivas-Perez, M.; Delbrueck, T.; Liu, S.C.; Douglas, R.; Hafliger, P.; Jimenez-Moreno, G.; Civit Ballcels, A.; Serrano-Gotarredona, T.; Acosta-Jimenez, A.J.; Linares-Barranco, B. CAVIAR: A 45K Neuron, 5M Synapse, 12G Connects/s AER Hardware sensory-processing-learning-actuating system for high-speed visual object recognition and tracking. IEEE Trans. Neural Netw. 2009, 20, 1417-1438.
44. Fukushim, K. Visual feature extraction by a multilayered network of analog threshold elements. IEEE Trans. Syst. Sci. Cyb. 1969, SSC5, 322-333.
45. Camunas-Mesa, L.; Perez-Carrasco, J.; Zamarreno-Ramos, C.; Serrano-Gotarredona, T.; Linares-Barranco, B. On Scalable Spiking ConvNet Hardware for Cortex-Like Visual Sensory Processing Systems. In Proceedigns of 2010 IEEE International Symposium on Circuits and Systems, Paris, France, 30 May-2 June 2010; pp. 249-252.
46. Perez-Carrasco, J.; Serrano, C.; Acha, B.; Serrano-Gotarredona, T.; Linares-Barranco, B. Spike-Based Convolutional Network for Real-Time Processing. In Proceedings of 20th International Conference on Pattern Recognition(ICPR 2010), Istanbul, Turkey, 23-26 August 2010; pp. 3085-3088.
47. Pardo, F.; Boluda, J.A.; Vegara, F.; Zuccarello, P. On the advantages of asynchronous pixel reading and processing for high-speed motion estimation. Lect. Note. Comput. Sci. 2008, 5358, 205-215.
48. Vegara, F.; Boluda, J.A.; Domingo, J.; Pardo, F.; Benavent, X. Accelerating Motion Analysis Algorithms with a Pixel Change-Driven Scheme. In Proceedings of The 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition 2009, IPCV 2009, Las Vegas, NV, USA, 13-16 July 2009; pp. 895-900.
49. Boluda, J.A.; Vegara, F.; Pardo, F.; Zuccarello, P. Selective change-driven image processing: A speeding-up strategy. Lect. Note. Comput. Sci. 2009, 5856, 37-44.
50. Pardo, F.; Zuccarello, P.; Boluda, J.A.; Vegara, F. Advantages of Selective Change Driven Vision for resource-limited systems. IEEE Trans. Circ. Syst. Vid. 2011, 21, 1415-1423.
51. Zuccarello, P.; Pardo, F.; de la Plaza, A.; Boluda, J.A. 32 × 32 winner-take-all matrix with single winner selection. Electron. Lett. 2010, 46, 333-335.
52. Serrano-Gotarredona, T.; Andreou, A.; Linares-Barranco, B. AER image filtering architecture for vision-processing systems. IEEE Trans. Circuit. Syst. 1999, 46, 1064-1071.
53. Serrano-Gotarredona, R.; Serrano-Gotarredona, T.; Acosta-Jimenez, A.; Linares-Barranco, B. A neuromorphic cortical-layer microchip for spike-based event processing vision systems. IEEE Trans. Circuit. Syst. 2006, 53, 2548-2566.
54. Serrano-Gotarredona, R.; Serrano-Gotarredona, T.; Acosta-Jimenez, A.; Serrano-Gotarredona, C.; Perez-Carrasco, J.; Linares-Barranco, B.; Linares-Barranco, A.; Jimenez-Moreno, G.; Civit-Ballcels, A. On real-time AER 2-D convolutions hardware for neuromorphic spike-based cortical processing. IEEE Trans. Neural Netw. 2008, 19, 1196-1219.
55. Perez-Carrasco, J.; Acha, B.; Serrano, C.; Camunas-Mesa, L.; Serrano-Gotarredona, T.; Linares-Barranco, B. Fast vision through frameless event-based sensing and convolutional processing: Application to texture recognition. IEEE Trans. Neural Netw. 2010, 21, 609-620.
56. Gomez-Rodriguez, F.; Miro-Amarante, L.; Diaz-del Rio, F.; Linares-Barranco, A.; Jimenez, G. Live Demonstration: Real Time Objects Tracking Using a Bio-inspired Processing Cascade Architecture. In Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), Paris, France, 30 May-2 June 2010; p. 1398.
57. Botella, G.; Garcia, A.; Rodriguez-Alvarez, M.; Ros, E.; Meyer-Baese, U.; Molina, M.C. Robust bioinspired architecture for optical-flow computation. IEEE Trans. VLSI Syst 2010, 18, 616-629.
58. Barranco, F.; Diaz, J.; Ros, E.; del Pino, B. Visual system based on artificial retina for motion detection. IEEE Trans. Syst. Man. Cy. B 2009, 39, 752-762.