A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision.

Depth from defocus is an important mechanism that enables vision systems to perceive depth. While machine vision has developed several algorithms to estimate depth from the amount of defocus present at the focal plane, existing techniques are slow, energy demanding and mainly relying on numerous acquisitions and massive amounts of filtering operations on the pixels' absolute luminance value. Recent advances in neuromorphic engineering allow an alternative to this problem, with the use of event-based silicon retinas and neural processing devices inspired by the organizing principles of the brain.

Effects of Cooling on the SNR and Contrast Detection of a Low-Light Event-Based Camera.

Johnson-Nyquist noise is the electronic noise generated by the thermal agitation of charge carriers, which increases when the sensor overheats. Current high-speed cameras used in low-light conditions are often cooled down to reduce thermal noise and increase their signal to noise ratio. These sensors, however, record hundreds of frames per second, which takes time, requires energy, and heavy computing power due to the substantial data load.

Full-field OCT technique for high speed event-based optical flow and particle tracking.

This article introduces a method to extract the speed and density of microparticles in real time at several kHz using an asynchronous event-based camera mounted on a full-field optical coherence tomography (FF-OCT) setup. These cameras detect significant amplitude changes, allowing scene-driven acquisitions. They are composed of an array of autonomously operating pixels.