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° of 2D pose.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9887 LNCS, pp. 455–462). Springer Verlag. https://doi.org/10.1007/978-3-319-44781-0_54
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