Abstract
This paper deals with the issue of evaluating and analyzing geometric point sets in three-dimensional space. Point sets or point clouds are often the product of 3D scanners and depth sensors, which are used in the field of autonomous movement for robots and vehicles. Therefore, for the classification of point sets within an active motion, not fully generated point clouds can be used, but knowledge can be extracted from the raw impulses of the respective time points. Attractors consisting of a continuum of stationary states and hysteretic memories can be used to couple multiple inputs over time given non-independent output quantities of a classifier and applied to suitable neural networks. In this paper, we show a way to assign input point clouds to sets of classes using hysteretic memories, which are transferable to neural networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
In passive Sonar, the target object itself rather than the sensing device emits a sound signal. This signal can be identified by its characteristic signal profile.
References
Aggarwal, C.C., Reddy, C.K.: Data Clustering - Algorithms and Applications. CRC Press, Boca Raton (2013)
AI and Robotics for GeoEnvironmental Modeling and Monitoring (AIRGEMM). https://tu-freiberg.de/airgemm Accessed 12 Mar 2021
Kisner., H., Thomas., U.: Segmentation of 3d point clouds using a new spectral clustering algorithm without a-priori knowledge. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 4m VISAPP, pp. 315–322. INSTICC, SciTePress (2018). https://doi.org/10.5220/0006549303150322
Nakagawa, M.: Point cloud clustering using panoramic layered range image. IntechOpen (2018). https://doi.org/10.5772/intechopen.76407
Carrillo, J., Perthame, B., Salort, D., Smets, D.: Qualitative properties of solutions for the noisy integrate and fire model in computational neuroscience. Comput. Neurosci. Nonlinearity 28(9), 3365–3388 (2015)
Comsa, I. M., Potempa, K., Versari, L., Fischbacher, T., Gesmundo, A., Alakuijala, J.: Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation (2020). https://arxiv.org/pdf/1907.13223.pdf
Eliasmith, C.: A unified approach to building and controlling spiking attractor networks. Neural Comput. 17(6), 1276–1314 (2005)
Farrokh, M., Dizaji, M., Dizaji, F., Moradinasab, N.: Universal hysteresis identification using extended Preisach Neural Network (2019). https://arxiv.org/pdf/2001.01559.pdf
Fusi, S.: Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol. Cybern. 87(5–6), 459–470 (2002)
Kuznetsov, N., Reitmann, V.: Attractor Dimension Estimates for Dynamical Systems: Theory and Computation. ECC, vol. 38. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50987-3
Mutto, C.D., Zanuttigh, P., Cortelazzo, G.M.: Time-of-Flight Cameras and Microsoft Kinect™; Springer, Boston (2012) https://doi.org/10.1007/978-1-4614-3807-6_2
Neimark, Yu. I.: On Lyapunov stability of systems with distributed wave units. Uchenye Zapiski Gor’kovskogo Gos. Universiteta, Ser. Fiz., XVI (1950) (Russian)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. abs/1612.00593 (2016). http://arxiv.org/abs/1612.00593
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. abs/1706.02413 (2017). http://arxiv.org/abs/1706.02413
Rajpura, P.S., Goyal, M., Bojinov, H., Hegde, R.S.: Dataset augmentation with synthetic images improves semantic segmentation. CoRR abs/1709.00849 (2017). http://arxiv.org/abs/1709.00849
Reitmann, S., Neumann, L., Jung, B.: BLAINDER—a blender AI add-on for generation of semantically labeled depth-sensing data. Sensors 21, 2144 (2021). https://doi.org/10.3390/s21062144
Reitmann, V.: Convergence in evolutionary variational inequalities with hysteresis nonlinearities. In: Proceedings of Equadiff 11, Bratislava, pp. 395–404 (2005)
Reitmann, V.: Realization theory methods for the stability investigation of nonlinear infinite-dimensional input-output systems. Mathematica BOHEMICA 136(2), 185–194 (2011)
Reitmann, V., Zyryanov, D.: The global attractor of a multivalued dynamical system generated by a two-phase heating problem. Differ. Eqn. Control Processes 4, 118–138 (2017). (Russian)
Seeholzer, A., Deger, M., Gerstner, W.: Stability of working memory in continuous attractor networks under the control of short-term plasticity. PLoS Comput. Biol. 15(4), e1006928 (2019)
Smirnova, V.B.: On the asymptotic behavior of a class of control systems with distributed parameters. Avtomatika i Telemekhanika 10, 5–12 (1973). (Russian)
Visintin, A.: Differential Models of Hysteresis. Springer, Berlin (1994). https://doi.org/10.1007/978-3-662-11557-2
Jyh-Da, W., Chuen-Tsai, S.: Constructing hysteretic memory in neural networks. IEEE Trans. Syst. Man Cybern. Part B 30(4), 601–609 (2000) Journal 2(5), 99–110 (2016)
Yi, L., et al.: A scalable active framework for region annotation in 3d shape collections. ACM Trans. Graph. 35(6) (2016). https://doi.org/10.1145/2980179.2980238
Acknowledgement
Part 3 and 4 of the work are supported by the 2020–2021 program Leading Scientific Schools of the Russian Federation (project NSh-2624.2020.1) and Saint Petersburg State University (ID 75206671).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Reitmann, S., Kudryashova, E.V., Jung, B., Reitmann, V. (2021). Classification of Point Clouds with Neural Networks and Continuum-Type Memories. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-79150-6_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79149-0
Online ISBN: 978-3-030-79150-6
eBook Packages: Computer ScienceComputer Science (R0)