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When Conventional machine learning meets neuromorphic engineering: Deep Temporal Networks (DTNets) a machine learning frawmework allowing to operate on Events and Frames and implantable on Tensor Flow Like Hardware.

, , , , and . CoRR, (2018)

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Live demonstration: Neuromorphic event-based multi-kernel algorithm for high speed visual features tracking., , , , and . BioCAS, page 178. IEEE, (2014)Event-Based Structured Light for Depth Reconstruction using Frequency Tagged Light Patterns., , and . CoRR, (2018)Image Sensor Model Using Geometric Algebra: From Calibration to Motion Estimation., , and . Geometric Algebra Computing, Springer, (2010)Plenoptic cameras in real-time robotics., , , , and . Int. J. Robotics Res., 32 (2): 206-217 (2013)When Conventional machine learning meets neuromorphic engineering: Deep Temporal Networks (DTNets) a machine learning frawmework allowing to operate on Events and Frames and implantable on Tensor Flow Like Hardware., , , , and . CoRR, (2018)Event-Based Features Selection and Tracking from Intertwined Estimation of Velocity and Generative Contours., , and . CoRR, (2018)Real-Time High Speed Motion Prediction Using Fast Aperture-Robust Event-Driven Visual Flow., , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (1): 361-372 (2022)Using structures to synchronize cameras of robots swarms., , and . IROS, page 3266-3271. IEEE, (2008)Event-based Dynamic Face Detection and Tracking Based on Activity., , and . CoRR, (2018)Asynchronous frameless event-based optical flow., , , , and . Neural Networks, (2012)