Papers
Topics
Authors
Recent
2000 character limit reached

Quantifying Noise of Dynamic Vision Sensor (2404.01948v1)

Published 2 Apr 2024 in cs.CV

Abstract: Dynamic visual sensors (DVS) are characterized by a large amount of background activity (BA) noise, which it is mixed with the original (cleaned) sensor signal. The dynamic nature of the signal and the absence in practical application of the ground truth, it clearly makes difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. In this letter, a new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA). The proposed technique can be used to address an existing DVS issues, which is how to quantitatively characterised noise and signal without ground truth, and how to derive an optimal denoising filter parameters. The solution of the latter problem is demonstrated for the popular real moving-car dataset.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (31)
  1. G. Gallego, T. Delbrück, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. J. Davison, J. Conradt, K. Daniilidis, and D. Scaramuzza, “Event-based vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 1, pp. 154–180, 2022.
  2. G. Gallego, M. Gehrig, and D. Scaramuzza, “Focus is all you need: Loss functions for event-based vision,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12 272–12 281, 2019.
  3. M. Muglikar, M. Gehrig, D. Gehrig, and D. Scaramuzza, “How to calibrate your event camera,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1403–1409, 2021.
  4. S. Shiba, Y. Aoki, and G. Gallego, “Secrets of event-based optical flow,” in European Conference on Computer Vision, 2022.
  5. R. Graça, B. Mcreynolds, and T. Delbrück, “Optimal biasing and physical limits of dvs event noise,” ArXiv, vol. abs/2304.04019, 2023.
  6. ——, “Shining light on the dvs pixel: A tutorial and discussion about biasing and optimization,” ArXiv, vol. abs/2304.04706, 2023.
  7. T. Delbruck, “Frame-free dynamic digital vision,” in International Symposium on Secure-Life Electronics, vol. 1, no. 1.   University of Tokyo, March 2008, pp. 21–26, in: Proceedings of International Symposium on Secure-Life Electronics, Advanced Electronics for Quality Life and Society, Univ. of Tokyo, Mar. 6-7, 2008. [Online]. Available: https://doi.org/10.5167/uzh-17620
  8. H. Liu, C. Brandli, C. Li, S.-C. Liu, and T. Delbrück, “Design of a spatiotemporal correlation filter for event-based sensors,” 2015 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 722–725, 2015.
  9. S. J. Ding, J. Chen, Y. Wang, Y. Kang, W. Song, J. Cheng, and Y. Cao, “E-mlb: Multilevel benchmark for event-based camera denoising,” ArXiv, vol. abs/2303.11997, 2023. [Online]. Available: https://arxiv.org/abs/2303.11997
  10. A. Khodamoradi and R. Kastner, “O(n)-space spatiotemporal filter for reducing noise in neuromorphic vision sensors,” IEEE Transactions on Emerging Topics in Computing, 2018.
  11. S. Guo and T. Delbruck, “Low cost and latency event camera background activity denoising,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
  12. Y. Feng, H. Lv, H. Liu, Y. Zhang, Y. Xiao, and C. Han, “Event density based denoising method for dynamic vision sensor,” Applied Sciences, 2020.
  13. X. Lagorce, G. Orchard, F. Galluppi, B. E. Shi, and R. B. Benosman, “Hots: a hierarchy of event-based time-surfaces for pattern recognition,” IEEE transactions on pattern analysis and machine intelligence, pp. 1346–1359, 2016.
  14. R. W. Baldwin, M. Almatrafi, J. R. Kaufman, V. Asari, and K. Hirakawa, “Inceptive event time-surfaces for object classification using neuromorphic cameras,” in Image Analysis and Recognition, F. Karray, A. Campilho, and A. Yu, Eds.   Cham: Springer International Publishing, 2019, pp. 395–403.
  15. J. Wu, C. Ma, L. Li, W. Dong, and G. Shi, “Probabilistic undirected graph based denoising method for dynamic vision sensor,” IEEE Transactions on Multimedia, vol. 23, pp. 1148–1159, 2021.
  16. R. W. Baldwin, M. Almatrafi, V. Asari, and K. Hirakawa, “Event probability mask (epm) and event denoising convolutional neural network (edncnn) for neuromorphic cameras,” 2020.
  17. H. Fang, J. Wu, L. Li, J. Hou, W. Dong, and G. Shi, “Aednet: Asynchronous event denoising with spatial-temporal correlation among irregular data,” in Proceedings of the 30th ACM International Conference on Multimedia, ser. MM ’22.   New York, NY, USA: Association for Computing Machinery, 2022, p. 1427–1435. [Online]. Available: https://doi.org/10.1145/3503161.3548048
  18. G. Gallego, H. Rebecq, and D. Scaramuzza, “A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 3867–3876.
  19. N. Xu, L. Wang, J. Zhao, and Z. Yao, “Denoising for dynamic vision sensor based on augmented spatiotemporal correlation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 9, pp. 4812–4824, 2023.
  20. C. Yang, H. Feng, Z. hai Xu, Q. Li, and Y. ting Chen, “The spatial correlation problem of noise in imaging deblurring and its solution,” J. Vis. Commun. Image Represent., vol. 56, pp. 167–176, 2018.
  21. C.-K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger, “Mosaic organization of dna nucleotides,” Phys. Rev. E, vol. 49, pp. 1685–1689, Feb 1994. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevE.49.1685
  22. B. Rogers, D. Giles, N. Draper, O. Hoos, and T. Gronwald, “A new detection method defining the aerobic threshold for endurance exercise and training prescription based on fractal correlation properties of heart rate variability,” Front. Physiol., vol. 11, p. 596567, 2020.
  23. T. Kataoka, T. Miyaguchi, and T. Akimoto, “Detrended fluctuation analysis of earthquake data,” Phys. Rev. Res., vol. 3, p. 033081, Jul 2021. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevResearch.3.033081
  24. X. Gou, M. Chen, and G. Zhang, “Time correlations of lightning flash sequences in thunderstorms revealed by fractal analysis,” Journal of Geophysical Research: Atmospheres, vol. 123, no. 2, pp. 1351–1362, 2018. [Online]. Available: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017JD027206
  25. K. Shrestha, “Multifractal detrended fluctuation analysis of return on bitcoin*,” International Review of Finance, vol. 21, no. 1, pp. 312–323, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/irfi.12256
  26. J. Alvarez-Ramirez, E. Rodriguez, I. Cervantes, and J. Carlos Echeverria, “Scaling properties of image textures: A detrending fluctuation analysis approach,” Physica A: Statistical Mechanics and its Applications, vol. 361, no. 2, pp. 677–698, 2006. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0378437105007193
  27. K. Hu, P. C. Ivanov, Z. Chen, P. Carpena, and H. Eugene Stanley, “Effect of trends on detrended fluctuation analysis,” Physical Review E, vol. 64, no. 1, Jun. 2001. [Online]. Available: http://dx.doi.org/10.1103/PhysRevE.64.011114
  28. J. W. Kantelhardt, E. Koscielny-Bunde, H. H. Rego, S. Havlin, and A. Bunde, “Detecting long-range correlations with detrended fluctuation analysis,” Physica A: Statistical Mechanics and its Applications, vol. 295, no. 3, pp. 441–454, 2001. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0378437101001443
  29. M. S. Taqqu, V. Teverovsky, and W. Willinger, “Estimators for long-range dependence: An empirical study,” Fractals, vol. 03, no. 04, pp. 785–798, 1995. [Online]. Available: https://doi.org/10.1142/S0218348X95000692
  30. M. Höll, K. Kiyono, and H. Kantz, “Theoretical foundation of detrending methods for fluctuation analysis such as detrended fluctuation analysis and detrending moving average,” Phys. Rev. E, vol. 99, p. 033305, Mar 2019. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevE.99.033305
  31. L. Kristoufek, “Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series,” Physica A: Statistical Mechanics and its Applications, vol. 406, pp. 169–175, 2014. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S037843711400209X
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now

Whiteboard

Paper to Video (Beta)

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up