Event Density Based Denoising Method for Dynamic Vision Sensor
Source abstract
Abstract
Dynamic vision sensor (DVS) is a new type of image sensor, which has application prospects in the fields of automobiles and robots. Dynamic vision sensors are very different from traditional image sensors in terms of pixel principle and output data. Background activity (BA) in the data will affect image quality, but there is currently no unified indicator to evaluate the image quality of event streams. This paper proposes a method to eliminate background activity, and proposes a method and performance index for evaluating filter performance: noise in real (NIR) and real in noise (RIN). The lower the value, the better the filter. This evaluation method does not require fixed pattern generation equipment, and can also evaluate filter performance using natural images. Through comparative experiments of the three filters, the comprehensive performance of the method in this paper is optimal. This method reduces the bandwidth required for DVS data transmission, reduces the computational cost of target extraction, and provides the possibility for the application of DVS in more fields.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: CCD and CMOS Imaging Sensors · Advanced Memory and Neural Computing · Infrared Target Detection Methodologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
References (22)
Show source-provided references
1. Delbrück, T., Linares-Barranco, B., Culurciello, E., and Posch, C. (June, January 30). Activity-Driven, Event-Based Vision Sensors. Proceedings of the 2010 IEEE International Symposium on Circuits and Systems, Paris, France.10.1109/ISCAS.2010.5537149 ↗
2. A 128 × 128 120db 15us latency asynchronous temporal contrast vision sensor10.1109/JSSC.2007.914337 ↗
3. Guo, M., Huang, J., and Chen, S. (207, January 28–31). Live demonstration: A 768 × 640 pixels 200meps dynamic vision sensor. Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA.10.1109/ISCAS.2017.8050397 ↗
4. Czech, D., and Orchard, G. (2016, January 26–29). Evaluating noise filtering for event-based asynchronous change detection image sensors. Proceedings of the 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), Singapore.10.1109/BIOROB.2016.7523452 ↗
5. Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A., Conradt, J., and Daniilidis, K. (2019). Event-Based Vision: A Survey. arXiv.
6. Weikersdorfer, D., Adrian, D.B., Cremers, D., and Conradt, J. (June, January 31). Event-based 3d slam with a depth-augmented dynamic vision sensor. Proceedings of the 2014 IEEE International Conference on Robotics and Automation, Hong Kong, China.10.1109/ICRA.2014.6906882 ↗
7. Ahn, E.Y., Lee, J.H., Mullen, T., and Yen, J. (2011, January 11–15). Dynamic vision sensor camera based bare hand gesture recognition. Proceedings of the 2011 IEEE Symposium On Computational Intelligence For Multimedia, Signal And Vision Processing, Paris, France.10.1109/CIMSIVP.2011.5949251 ↗
8. Lee, K., Ryu, H., Park, S., Lee, J.H., Park, P., Shin, C., Woo, J., Kim, T., and Kang, B. (2012, January 2–5). Four dof gesture recognition with an event-based image sensor. Proceedings of the 1st IEEE Global Conference on Consumer Electronics 2012, Tokyo, Japan.10.1109/GCCE.2012.6379606 ↗
9. Characterization setup for event-based imagers applied to modulated light signal detection10.1364/AO.58.001305 ↗
10. Asynchronous corner detection and tracking for event cameras in real time10.1109/LRA.2018.2849882 ↗
11. Mueggler, E., Huber, B., and Scaramuzza, D. (2014, January 14–18). Event-based, 6-dof pose tracking for high-speed maneuvers. Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.10.1109/IROS.2014.6942940 ↗
12. American sign language alphabet recognition using a neuromorphic sensor and an artificial neural network10.3390/s17102176 ↗
13. Barrios-Avilés, J., Rosado, A., Medus, L., Bataller-Mompeán, M., and Guerrero Martinez, J. (2018). Less data same information for event-based sensors: A bioinspired filtering and data reduction algorithm. Sensors, 18.10.3390/s18124122 ↗
14. (2020, March 05). Celepixel Technology Co. Ltd.. Available online: http://www.celepixel.com.
15. (2020, March 05). Samsung Smartthings Vision. Available online: https://www.samsung.com/au/smart-home/smartthings-vision-u999/.
16. Temperature and parasitic photocurrent effects in dynamic vision sensors10.1109/TED.2017.2717848 ↗
17. Jing, H., Guo, M., and Chen, S. (2017, January 28–31). A dynamic vision sensor with direct logarithmic output and full-frame picture-on-demand. Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA.
18. Liu, H., Brandli, C., Li, C., Liu, S., and Delbruck, T. (2015, January 24–27). Design of a spatiotemporal correlation filter for event-based sensors. Proceedings of the 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, Portugal.10.1109/ISCAS.2015.7168735 ↗
19. Event-based visual flow10.1109/TNNLS.2013.2273537 ↗
20. Brändli, C.P. (2015). Event-Based Machine Vision. [Ph.D. Thesis, ETH Zurich].
21. Khodamoradi, A., and Kastner, R. (2018). O(n)-space spatiotemporal filter for reducing noise in neuromorphic vision sensors. IEEE Trans. Emerg. Top. Comput.10.1109/TETC.2017.2788865 ↗
22. Delbrück, T. (2008, January 6–7). Frame-free dynamic digital vision. Proceedings of the International Symposium on Secure-Life Electronics, Advanced Electronics for Quality Life and Society, Tokyo, Japan.