Abstract
Neuromorphic image sensors produce activity-driven spiking output at every pixel. These low-power consuming imagers which encode visual change information in the form of spikes help reduce computational overhead and realize complex real-time systems; object recognition and pose-estimation to name a few. However, there exists a lack of algorithms in event-based vision aimed towards capturing invariance to transformations. In this work, we propose a methodology for recognizing objects invariant to their pose with the Dynamic Vision Sensor (DVS). A novel slow-ELM architecture is proposed which combines the effectiveness of Extreme Learning Machines and Slow Feature Analysis. The system, tested on an Intel Core i5-4590 CPU, can perform 10, 000 classifications per second and achieves 1 % classification error for 8 objects with views accumulated over 90 \(^\circ \) of 2D pose.
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References
Ni, Z., Bolopion, A., Agnus, J., Benosman, R., Regnier, S.: Asynchronous event-based visual shape tracking for stable haptic feedback in microrobotics. IEEE Trans. Robot. 28(5), 1081–1089 (2012)
Perez-Carrasco, J.A., Zhao, B., Serrano, C., Acha, B., Serrano-Gotarredona, T., Chen, S., Linares-Barranco, B.: Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate coding and coincidence processing-application to feedforward convnets. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2706–2719 (2013)
Valeiras, D.R., Orchard, G., Ieng, S.H., Benosman, R.B.: Neuromorphic event-based 3d pose estimation. Front. Neurosci. 9, 522 (2015)
Huang, F.J., Zhang, H.J., Chen, T., Zhou, Z.: Pose invariant face recognition. Institute of Electrical and Electronics Engineers Inc., March 2000
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(13), 489–501 (2006). Neural Networks Selected Papers from the 7th Brazilian Symposium on Neural Networks
Wiskott, L., Sejnowski, T.: Slow feature analysis: unsupervised learning of invariances. Neural Comput. 14(4), 715–770 (2002)
Berkes, P., Wiskott, L.: Slow feature analysis yields a rich repertoire of complex cell properties. J. Vis. 5(6), 9 (2005)
Franzius, M., Wilbert, N., Wiskott, L.: Invariant object recognition with slow feature analysis. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 961–970. Springer, Heidelberg (2008)
Ghosh, R., Mishra, A., Orchard, G., Thakor, N.V.: Real-time object recognition and orientation estimation using an event-based camera and CNN. In: Biomedical Circuits and Systems Conference (BioCAS), October 2014, pp. 544–547. IEEE (2014)
Zhang, H., Yin, Y., Zhang, S., Sun, C.: An improved ELM algorithm based on PCA technique. In: Proceedings of ELM-2014, Volume 2: Applications, pp. 95–104. Springer International Publishing, Cham (2015)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 513–529 (2012)
Thomas, A., Ferrar, V., Leibe, B., Tuytelaars, T., Schiel, B., Van Gool, L.: Towards multi-view object class detection. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1589–1596 (2006)
Acknowledgements
This work is supported by a NPRP grant from the Qatar National Research Fund under the grant No. NPRP 7-673-2-251. The statements made herein are solely the responsibility of the authors.
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Ghosh, R., Siyi, T., Rasouli, M., Thakor, N.V., Kukreja, S.L. (2016). Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_54
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DOI: https://doi.org/10.1007/978-3-319-44781-0_54
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