A Dynamic Vision Sensor With 1% Temporal Contrast Sensitivity and In-Pixel Asynchronous Delta Modulator for Event Encoding

Yang, Minhao; Liu, Shih‐Chii; Delbrück, Tobi

doi:10.1109/jssc.2015.2425886

Cited by 89 publications

(64 citation statements)

References 35 publications

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“…Asynchronous event-based cameras are bio-inspired sensors that consider intensity changes (events) in the scene with precise timestamps and a high dynamic range. DVS is one of the well known frame-free sensors with a high temporal resolution of few microseconds [43], [44] which is significantly faster than ordinary cameras. This vision sensor captures intensity changes logarithmic ally at each pixel rather than capturing the whole scene in a fixed interval.…”

Section: Event-driven Tactile Sensormentioning

confidence: 99%

A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications

Naeini

Alali

Al-Husari

et al. 2020

IEEE Trans. Instrum. Meas.

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In this paper, a novel Vision-Based Measurement (VBM) approach is proposed to estimate the contact force and classify materials in a single grasp. This approach is the first event-based tactile sensor which utilizes the recent technology of neuromorphic cameras. This novel approach provides a higher sensitivity, a lower latency, and less computational and power consumption compared to other conventional visionbased techniques. Moreover, Dynamic Vision Sensor (DVS) has a higher dynamic range which increases the sensor sensitivity and performance in poor lighting conditions. Two time-series machine learning methods, namely, Time Delay Neural Network (TDNN) and Gaussian Process (GP) are developed to estimate the contact force in a grasp. A Deep Neural Network (DNN) is proposed to classify the object materials. Forty-eight experiments are conducted for four different materials to validate the proposed methods and compare them against a piezoresistive force sensor measurements. A leave-one-out cross-validation technique is implemented to evaluate and analyze the performance of the proposed machine learning methods. The contact force is successfully estimated with a mean squared error of 0.16 N and 0.17 N for TDNN and GP respectively. Four materials are classified with an average accuracy of 79.17% using unseen experimental data. The results show the applicability of eventbased sensors for grasping applications.

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Section: Event-driven Tactile Sensormentioning

confidence: 99%

A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications

Naeini

Alali

Al-Husari

et al. 2020

IEEE Trans. Instrum. Meas.

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show abstract

“…DVS has evolved into several versions such as in Brandli et al (, ), wherein authors developed a Dynamic and Active Pixel Vision Sensor (DAVIS), which utilizes Active Pixel Sensor (APS) capability to enable overall intensity readout and Delbruck and Berner (), Bardallo et al (), Yang, Liu, & Delbruck, (), where research has been carried out to increase the contrast sensitivity.…”

Section: Silicon Retinamentioning

confidence: 99%

Neuromorphic vision: From sensors to event‐based algorithms

Lakshmi

Chakraborty

Thakur

2019

WIREs Data Min & Knowl

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Regardless of the marvels brought by the conventional frame‐based cameras, they have significant drawbacks due to their redundancy in data and temporal latency. This causes problem in applications where low‐latency transmission and high‐speed processing are mandatory. Proceeding along this line of thought, the neurobiological principles of the biological retina have been adapted to accomplish data sparsity and high dynamic range at the pixel level. These bio‐inspired neuromorphic vision sensors alleviate the more serious bottleneck of data redundancy by responding to changes in illumination rather than to illumination itself. This paper reviews in brief one such representative of neuromorphic sensors, the activity‐driven event‐based vision sensor, which mimics human eyes. Spatio‐temporal encoding of event data permits incorporation of time correlation in addition to spatial correlation in vision processing, which enables more robustness. Henceforth, the conventional vision algorithms have to be reformulated to adapt to this new generation vision sensor data. It involves design of algorithms for sparse, asynchronous, and accurately timed information. Theories and new researches have begun emerging recently in the domain of event‐based vision. The necessity to compile the vision research carried out in this sensor domain has turned out to be considerably more essential. Towards this, this paper reviews the state‐of‐the‐art event‐based vision algorithms by categorizing them into three major vision applications, object detection/recognition, object tracking, localization and mapping. This article is categorized under: Technologies > Machine Learning

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“…In this first publication, the authors already devised possible applications for the sensor, ie, traffic monitoring, gaming, and sleep monitoring, etc. In more recent publications, several authors presented improved versions of the original DVS …”

Section: Dynamic Vision Sensorsmentioning

confidence: 99%

Applications of event‐based image sensors—Review and analysis

Leñero‐Bardallo

Carmona-Galán

Rodríguez-Vázquez

2018

Circuit Theory & Apps

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The spread of event-driven asynchronous vision sensors during the last years has increased significantly the industrial interest and the application scenarios for them. This article reviews the main fields of application that event-based image sensors have found during the last 20 years. We focus in the description of applications where such devices can outperform conventional frame-based sensors. The practical functions of the three main families of asynchronous event-based sensors are analyzed. The article also studies what are the factors that increase nowadays the demand of sensors that minimize the power and bandwidth consumption. Moreover, the technological factors that have facilitated the development of asynchronous sensors are discussed.

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A Dynamic Vision Sensor With 1% Temporal Contrast Sensitivity and In-Pixel Asynchronous Delta Modulator for Event Encoding

Cited by 89 publications

References 35 publications

A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications

A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications

Neuromorphic vision: From sensors to event‐based algorithms

Applications of event‐based image sensors—Review and analysis

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