Arnon Amir

TL;DR: Inspired by the brain’s structure, an efficient, scalable, and flexible non–von Neumann architecture is developed that leverages contemporary silicon technology and is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification.

TL;DR: This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

TL;DR: This work presents 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).

TL;DR: Experimental results from a real-time implementation of the pupil detection technique show that this technique is very robust, and able to detect pupils using wide field of view low cost cameras under different illumination conditions, even for people with glasses, from considerable long distances.

TL;DR: In the NIST TRECVID-2004 evaluation as discussed by the authors, shot boundary detection, high-level feature detection, story segmentation, and search were all performed by the same team.