A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision

no code implementations • 28 Aug 2019 • John K. Tsotsos, Iuliia Kotseruba, Alexander Andreopoulos, Yulong Wu

This reveals a strong mismatch between optimal performance ranges of classical theory-driven algorithms and sensor setting distributions in the common vision datasets, while data-driven models were trained for those datasets.

A Low Power, High Throughput, Fully Event-Based Stereo System

no code implementations • CVPR 2018 • Alexander Andreopoulos, Hirak J. Kashyap, Tapan K. Nayak, Arnon Amir, Myron D. Flickner

We introduce a stereo correspondence system implemented fully on event-based digital hardware, using a fully graph-based non von-Neumann computation model, where no frames, arrays, or any other such data-structures are used.

Vocal Bursts Intensity Prediction

A Low Power, Fully Event-Based Gesture Recognition System

no code implementations • CVPR 2017 • Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Garreau, Marcela Mendoza, Jeff Kusnitz, Michael Debole, Steve Esser, Tobi Delbruck, Myron Flickner, Dharmendra Modha

We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS).

Gesture Recognition

Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing

no code implementations • 28 Mar 2016 • Steven K. Esser, Paul A. Merolla, John V. Arthur, Andrew S. Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J. Berg, Jeffrey L. McKinstry, Timothy Melano, Davis R. Barch, Carmelo Di Nolfo, Pallab Datta, Arnon Amir, Brian Taba, Myron D. Flickner, Dharmendra S. Modha

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks.