A distributed cognitive approach in cybernetic modelling of human vision in a robotic swarm

• Autorzy: Podpora Michal , Kawala-Sterniuk Aleksandra , Kovalchuk Viktoria , Bialic Grzegorz , Piekielny Pawel

Identyfikatory

DOI

10.1515/bams-2020-0025

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

biocybernetics; computer vision; robotics

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2020
• Tom: Vol. 16, no. 3
• Strony: art. no. 20200025
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Podpora Michal

• Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole, Poland

autor

Kawala-Sterniuk Aleksandra

• Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole, Poland

autor

Kovalchuk Viktoria

• Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole, Poland

autor

Bialic Grzegorz

• Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole, Poland

autor

Piekielny Pawel

• Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole, Poland

Bibliografia

• 1. Wandell B, Thomas S. Foundations of vision. Psyccritiques 1997; 42. https://doi.org/10.1037/000258.
• 2. Horn B, Klaus B, Horn P. Robot vision. MIT Press; 1986.
• 3. Jain R, Kasturi R, Schunck BG. Machine vision. New York: McGrawHill; 1995, vol 5, p. 309-64.
• 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.
• 5. Ballard DH, Zhang R. The hierarchical evolution in human vision modeling. Trends Cognit Sci 2020.
• 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.
• 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.
• 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.
• 9. Dickinson SJ, Leonardis A, Schiele B, Tarr MJ, editors. Object categorization: computer and human vision perspectives. Cambridge University Press; 2009.
• 10. Nixon M, Aguado A. Feature extraction and image processing for computer vision. Academic Press; 2019.
• 11. Li B, Qi X, Lukasiewicz T, Torr PH. ManiGAN: text-guided image manipulation. arXiv preprint arXiv:1912.06203; 2019.
• 12. Podpora M. Vision processing for autonomous robots with the use of distributed systems [Ph.D. dissertation]. Opole University of Technology; 2012 [in Polish].
• 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.
• 14. Hawkins J, Blakeslee S. On intelligence. Times Books; 2004.
• 15. Numenta. Hierarchical temporal memory including HTM cortical learning algorithms. https://numenta.com/assets/pdf/ whitepapers/hierarchical-temporal-memory-cortical-learningalgorithm-0.2.1-en.pdf [Accessed Sep 2015].
• 16. Rozanska A, Podpora M. Multimodal sentiment analysis applied to interaction between patients and a humanoid robot Pepper. IFAC-PapersOnLine 2019;52:411-14.
• 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.
• 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.
• 19. Podpora M, Gardecki A, Beniak R, Klin B, Vicario JL, KawalaSterniuk A. Human interaction smart subsystem-extending speech-based human-robot interaction systems with an implementation of external smart sensors. Sensors 2020;20:2376.
• 20. Campbell ME, Whitacre WW. Cooperative tracking using vision measurements on seascan UAVs. IEEE Trans Control Syst Technol 2007;15:613-26.
• 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.
• 22. Rioux A, Esteves C, Hayet JB, Suleiman W. Cooperative visionbased object transportation by two humanoid robots in a cluttered environment. Int J Hum Robot 2017;14:1750018.
• 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.
• 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.
• 25. Podpora M, Kawala-Janik A, Pelc M. Policy-based selfconfiguration 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.
• 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.
• 27. Podpora M. Fuzzified operator language. Conference proceedings of X TERW; 2015.
• 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.
• 29. Lee K. Teachable object recognizers for the blind: using firstperson vision. ACM SIGACCESS - Accessibility and Computing; 2020, 1-1.
• 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.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).