A distributed cognitive approach in cybernetic modelling of human vision in a robotic swarm
-
Michal Podpora
,
Aleksandra Kawala-Sterniuk
Abstract
Objectives
In this paper a novel approach regarding image analysis in Machine Vision applications was proposed.
Methods
The presented concept consists of two issues: (1) shifting some of the complex image processing and understanding algorithms from a mobile robot to distributed computer, and (2) designing the cognitive system (in a distributed computer) in such a way, that it would be common for numerous robots. The authors of this work focused on image processing, and they propose to accelerate vision understanding by using Cooperative Vision (CoV), i.e., to get video input from cooperating robots and process it in a centralized system.
Results
To verify the purposefulness of such approach, a comparative study is currently being conducted, involving a classical single-camera Computer Vision (CV) mobile robot and two (or more) single-camera CV robots cooperating in CoV mode.
Conclusions
The CoV system is being designed and implemented so that the algorithm would be able to utilize multiple video sources for recognition of objects on the very same scene.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Ethical approval: The conducted research is not related to either human or animal use.
References
1. Wandell, B, Thomas, S. Foundations of vision. Psyccritiques 1997;42. https://doi.org/10.1037/000258.Search in Google Scholar
2. Horn, B, Klaus, B, Horn, P. Robot vision. MIT Press; 1986.Search in Google Scholar
3. Jain, R, Kasturi, R, Schunck, BG. Machine vision. New York: McGraw-Hill; 1995, vol 5, p. 309–64.Search in Google Scholar
4. Maliamanis, T, Papakostas, GA. Adversarial computer vision: a current snapshot. In Twelfth International Conference on Machine Vision (ICMV 2019). International Society for Optics and Photonics; 2020, vol 11433, p. 1143328.10.1117/12.2559582Search in Google Scholar
5. Ballard, DH, Zhang, R. The hierarchical evolution in human vision modeling. Trends Cognit Sci 2020.10.1111/tops.12527Search in Google Scholar PubMed
6. Ye, XW, Jin, T, Ang, PP. Computer vision-based monitoring of ship navigation for bridge collision risk assessment. In Machine vision and navigation. Cham: Springer; 2020. p. 787–807.10.1007/978-3-030-22587-2_26Search in Google Scholar
7. Preet Kour V, Arora S. Vision based techniques for image classification: a survey. Sakshi, vision based techniques for image classification: a survey (March 28, 2020); 2020.10.2139/ssrn.3562965Search in Google Scholar
8. Bagi, R, Dutta, T, Gupta, HP. Deep learning architectures for computer vision applications: a study. In: Advances in data and information sciences. Singapore: Springer; 2020. p. 601–12.10.1007/978-981-15-0694-9_56Search in Google Scholar
9. Dickinson, SJ, Leonardis, A, Schiele, B, Tarr, MJ, editors. Object categorization: computer and human vision perspectives. Cambridge University Press; 2009.10.1017/CBO9780511635465Search in Google Scholar
10. Nixon, M, Aguado, A. Feature extraction and image processing for computer vision. Academic Press; 2019.10.1016/B978-0-12-814976-8.00003-8Search in Google Scholar
11. Li, B, Qi, X, Lukasiewicz, T, Torr, PH. ManiGAN: text-guided image manipulation. arXiv preprint arXiv:1912.06203; 2019.10.1109/CVPR42600.2020.00790Search in Google Scholar
12. Podpora, M. Vision processing for autonomous robots with the use of distributed systems [Ph.D. dissertation]. Opole University of Technology; 2012 [in Polish].Search in Google Scholar
13. Tadeusiewicz, R, editor. Theoretical neurocybernetics, chapter 14: DUCH W., cognitive architectures [in Polish]. Warszawa: Wydawnictwa Uniwersytetu Warszawskiego; 2009. ISBN: 978-83-235-0479-5.Search in Google Scholar
14. Hawkins, J, Blakeslee, S. On intelligence. Times Books; 2004.Search in Google Scholar
15. Numenta. Hierarchical temporal memory including HTM cortical learning algorithms. https://numenta.com/assets/pdf/whitepapers/ hierarchical-temporal-memory-cortical-learning-algorithm-0.2.1-en.pdf [Accessed Sep 2015].Search in Google Scholar
16. Rozanska, A, Podpora, M. Multimodal sentiment analysis applied to interaction between patients and a humanoid robot Pepper. IFAC-PapersOnLine 2019;52:411–14. https://doi.org/10.1016/j.ifacol.2019.12.696.Search in Google Scholar
17. Podpora, M, Gardecki, A, Kawala-Sterniuk, A. Humanoid receptionist connected to IoT subsystems and smart infrastructure is smarter than expected. IFAC-PapersOnLine 2019;52:347–52. https://doi.org/10.1016/j.ifacol.2019.12.685.Search in Google Scholar
18. Rozanska, A, Rachwaniec-Szczecinska, Z, Kawala-Janik, A, Podpora, M. Internet of Things embedded system for emotion recognition. In: 2018 IEEE 20th international conference on e-Health networking, applications and services (Healthcom). IEEE; 2018. p. 1–5.10.1109/HealthCom.2018.8531100Search in Google Scholar
19. Podpora, M, Gardecki, A, Beniak, R, Klin, B, Vicario, JL, Kawala-Sterniuk, A. Human interaction smart subsystem—extending speech-based human-robot interaction systems with an implementation of external smart sensors. Sensors 2020;20:2376. https://doi.org/10.3390/s20082376.Search in Google Scholar
20. Campbell, ME, Whitacre, WW. Cooperative tracking using vision measurements on seascan UAVs. IEEE Trans Control Syst Technol 2007;15:613–26. https://doi.org/10.1109/TCST.2007.899177.Search in Google Scholar
21. Yue, W, Hussein, II. Cooperative vision-based multi-vehicle dynamic coverage control for underwater applications. IEEE international conference on control applications; 2007. p. 82–7. https://doi.org/10.1109/CCA.2007.4389210.Search in Google Scholar
22. Rioux, A, Esteves, C, Hayet, JB, Suleiman, W. Cooperative vision-based object transportation by two humanoid robots in a cluttered environment. Int J Hum Robot 2017;14:1750018. https://doi.org/10.1142/S0219843617500189.Search in Google Scholar
23. Bethke, B, Valenti, M, How, J. Cooperative vision based estimation and tracking using multiple UAVs. Advances in cooperative control and optimization. Springer Berlin Heidelberg; 2007. p. 179–89.10.1007/978-3-540-74356-9_11Search in Google Scholar
24. Tiszbierek, A, Podpora, M. Overview of popular 3D imaging approaches for mobile robots and a pilot study on a low-cost 3D imaging system. Proceedings of Quaesti 2014 conference. Zilina: EDIS; 2014. p. 515–20. ISBN 978-80-554-0959-7.Search in Google Scholar
25. Podpora, M, Kawala-Janik, A, Pelc, M. Policy-based self-configuration of autonomous systems information inputs. Proceedings of the 2013 IEEE 7th conference on intelligent data acquisition and advanced computing systems (IDAACS). Berlin; 2013, vol 2, p. 845–8.10.1109/IDAACS.2013.6663047Search in Google Scholar
26. Podpora, M, Korbas, GP, Kawala-Janik, A. YUV vs RGB – choosing a color space for human-machine interaction. Annals of Computer Science and Information Systems; 2014, vol 3, p. 29–34. ISBN 978-83-60810-60-6, ISSN 2300-5963. https://doi.org/10.15439/2014F206.Search in Google Scholar
27. Podpora, M. Fuzzified operator language. Conference proceedings of X TERW; 2015.Search in Google Scholar
28. Ahmed, S., Balasubramanian, H., Stumpf, S., Morrison, C., Sellen, A., Grayson, M. Investigating the intelligibility of a computer vision system for blind users. In: Proceedings of the 25th international conference on intelligent user interfaces; 2020. p. 419–29.10.1145/3377325.3377508Search in Google Scholar
29. Lee, K. Teachable object recognizers for the blind: using first-person vision. ACM SIGACCESS – Accessibility and Computing; 2020, 1-1.10.1145/3386402.3386405Search in Google Scholar
30. Dhamani, N, Martin, G, Schubert, C, Singh, P, Hatten, N, Akella, MR. Applications of machine learning and monocular vision for autonomous on-orbit proximity operations. In: AIAA Scitech 2020 Forum; 2020:1376 p.10.2514/6.2020-1376Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Research Articles
- Overview of the holographic-guided cardiovascular interventions and training – a perspective
- Development of the low-cost, smartphone-based cardiac auscultation training manikin
- Cooperation of CUDA and Intel multi-core architecture in the independent component analysis algorithm for EEG data
- A distributed cognitive approach in cybernetic modelling of human vision in a robotic swarm
- Thingspeak-based respiratory rate streaming system for essential monitoring purposes
- Recognition of multifont English electronic prescribing based on convolution neural network algorithm
- Short Communication
- BatchDeconvolution: a Fiji plugin for increasing deconvolution workflow
Articles in the same Issue
- Research Articles
- Overview of the holographic-guided cardiovascular interventions and training – a perspective
- Development of the low-cost, smartphone-based cardiac auscultation training manikin
- Cooperation of CUDA and Intel multi-core architecture in the independent component analysis algorithm for EEG data
- A distributed cognitive approach in cybernetic modelling of human vision in a robotic swarm
- Thingspeak-based respiratory rate streaming system for essential monitoring purposes
- Recognition of multifont English electronic prescribing based on convolution neural network algorithm
- Short Communication
- BatchDeconvolution: a Fiji plugin for increasing deconvolution workflow
