A 128$\,\times$ 128 1.5% Contrast Sensitivity 0.9% FPN 3 µs Latency 4 mW Asynchronous Frame-Free Dynamic Vision Sensor Using Transimpedance Preamplifiers

By T. Serrano‐Gotarredona, B. Linares‐Barranco. IEEE Journal of Solid-State Circuits, 2013

307 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c609ad9f61d176381796589942d80b5734dbad81

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In Cellular and Molecular Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

HFirst: A Temporal Approach to Object Recognition (2015, methodology): “For the poker card task data was provided by LinaresBarranco [35] who captured the data using the sensitive DVS sensor [22].”
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades (2015, background): “Arguably the most complete dataset created thus far is the “MNIST-DVS” dataset5, which is recorded from an actual sensor (Serrano-Gotarredona and Linares-Barranco, 2013) viewing MNIST examples moving on a computer monitor.”
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification (2018, methodology): “N-MNIST, N-Caltech101, MNIST-DVS and CIFAR10DVS are four publicly available datasets created by converting the popular frame-based MNIST [32], Caltech101 [20] and CIFAR10 [27] to an event-based representation.”
HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition (2017, methodology): “This work uses datasets recorded by different event-based sensors [27], [23] and previously used in other publications [13], [14].”
Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output (2014, methodology): “Voltage headroom limits the practical number of stacked transistors to N 1⁄4 4 and the circuit reduces intrascene DR [52].”
A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor (2014, background): “An alternative arrangement used by [2] sources the photocurrent from a p-FET whose source is driven by feedback, while the gate is held at a common voltage; this will be called a common-gate photoreceptor.”
Memory and Information Processing in Neuromorphic Systems (2015, background): “Two main approaches are being pursued, depending on the readout scheme of the vision sensor: a frame-based approach, which uses inputs from conventional frame-based cameras, and an event-driven one, which uses inputs from event-driven retina-like vision sensors [52], [53], [54], [55].”
Event-Based Vision: A Survey (2019, methodology): “Higher Contrast Sensitivity: Efforts have been made to improve the temporal contrast sensitivity of event cameras, leading to experimental sensors with higher sensitivity [58], [59], [60] (down to laboratory condition ∼ 1%).”
CIFAR10-DVS: An Event-Stream Dataset for Object Classification (2017, background): “Neuromorphic vision based on neuromorphic cameras (Delbruck, 2008; Lichtsteiner et al., 2008; Serrano-Gotarredona and Linares-Barranco, 2013; Yang et al., 2015) which converts visual information to address-event-representation (AER; Boahen, 2000) event streams has garnered increasing attention.”