A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS

By Christoph Posch, Daniel Matolin, Rainer Wohlgenannt. IEEE Journal of Solid-State Circuits, 2011

786 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e9cc63056952e12cfc6cdec05ad26b2ceb5239f3

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In Cellular and Molecular Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Going Deeper in Spiking Neural Networks: VGG and Residual Architectures (2018, background): “This can be particularly appealing for NN implementations in low-power neuromorphic hardware specialized for SNNs (Merolla et al., 2014; Akopyan et al., 2015) or interfacing with silicon cochleas or event-driven sensors (Posch et al., 2011, 2014).”
Simultaneous Optical Flow and Intensity Estimation from an Event Camera (2016, methodology): “Previous computer vision work using event cameras, both the DVS and the related Asynchronous Time-based Image Sensor (ATIS) [19], has not aimed at such generic estimation, but instead focused on reduced problems.”
Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output (2014, methodology): “3 shows the basic block diagram of a typical DVS pixel circuit [49], [52], [61], [64].”
Event-Based Vision: A Survey (2019, background): “The Asynchronous Time Based Image Sensor ( ATIS ) [3], [48] has pixels that contain a DVS subpixel (called change detection CD) that triggers another subpixel to read out the absolute intensity (exposure measurement EM).”
High Speed and High Dynamic Range Video with an Event Camera (2019, methodology): “The N-CARS dataset [14] (recorded with an ATIS event camera [52]) proposes a binary classification task: deciding whether a car is visible or not using a 100ms sequence of events.”
Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera (2016, neutral): “Recently, cameras have been developed that interleave event data with conventional intensity frames (DAVIS [3]), or per-event intensity measurement (ATIS [21]).”
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 (2013, background): “2(c) shows the original photo transduction stage [17], [18], [23], [24] with , where is the subthreshold”
HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition (2017, methodology): “Over the past few years, a variety of these event-based devices has been developed, including temporal contrast vision sensors that are sensitive to relative luminance change [22], [23], [24], gradient-based sensors sensitive to static edges [25], and optical-flow sensors [26].”
Skimming Digits: Neuromorphic Classification of Spike-Encoded Images (2016, methodology): “A full treatment and in-depth review of these silicon retinae can be found in Posch et al. (2014) and the device used in this paper makes use of the Asynchronous Time-Based Imaging Sensor (ATIS) described in Posch et al. (2011).”
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification (2018, methodology): “This new principle leads to significant reduction of memory usage and of power consumption and the information contained in standard videos of hundreds megabytes can be naturally compressed in an event stream of few hundreds kilobytes [36, 52, 63].”
A Low Power, Fully Event-Based Gesture Recognition System (2017, background): “While there is a large body of work on hand gesture recognition, including many real-time systems [26, 47, 30], and a growing number of publications on event-based sensors like the DAVIS [2], ATIS [36], and DVS [22], there is very little work in the intersection of the two.”
HFirst: A Temporal Approach to Object Recognition (2015, methodology): “Notable examples of spiking AER vision sensors include the earliest examples of spiking silicon retinae by Culurciello et al. [18] and Zaghloul et al. [19], as well as the more recent Dynamic Vision Sensor (DVS) from Delbruck [21], the sensitive DVS from Linares-Berranco [22], and the Asynchronous T”
STDP and STDP variations with memristors for spiking neuromorphic learning systems (2013, background): “…2011), temporal contrast (Barbaro et al., 2002; Mallik et al., 2005; Chan et al., 2007a; Lichtsteiner et al., 2008; Leñero-Bardallo et al., 2011; Posch et al., 2011; Serrano-Gotarredona et al., 2013), motion (Kramer, 1996; Sarpeshkar et al., 1996; Ozalevli and Higgins, 2005), and spatial…”
Event-Based Visual Flow (2014, background): “This work builds on recent achievements of neuromorphic engineering, exploiting the unique characteristics of a novel family of asynchronous image sensors ([5], [6], [7], [8]).”
Memory and Information Processing in Neuromorphic Systems (2015, background): “They include convolutional networks which are being explored intensively within the neuromorphic community for visual processing tasks [46].”
Learning to Detect Objects with a 1 Megapixel Event Camera (2020, methodology): “We now compare our method with the state-of-the-art on the 1 Mpx Detection Dataset and the Gen1 Detection Dataset [5], which is another automotive dataset acquired with a QVGA event camera [50].”
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades (2015, methodology): “The second thread pulls ATIS data off the Opal Kelly over USB2.”