Event-Based Eccentric Motion Detection Exploiting Time Difference Encoding

D’Angelo, Giulia; Janotte, Ella; Schoepe, Thorben; O’Keeffe, James; Milde, Moritz B.; Chicca, Elisabetta; Bartolozzi, Chiara

doi:10.3389/fnins.2020.00451

Cited by 27 publications

(21 citation statements)

References 42 publications

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“…However, only few sensory devices exist today that directly provide event-driven data. Prominent examples are the dynamic vision sensor (DVS) used for vision applications such as tracking and motion detection 14 – 17 , the silicon cochlea 18 and neuromorphic auditory sensor (NAS) 19 , used for processing auditory signals, the olfactory sensor 20 , and multiple examples of touch sensors used for texture recognition 21 , 22 .…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic object localization using resistive memories and ultrasonic transducers

et al. 2022

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Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary Metal-Oxide Semiconductor neuromorphic architectures provide an ideal hardware substrate for such tasks. To demonstrate the full potential of such systems, we propose and experimentally demonstrate an end-to-end sensory processing solution for a real-world object localization application. Drawing inspiration from the barn owl’s neuroanatomy, we developed a bio-inspired, event-driven object localization system that couples state-of-the-art piezoelectric micromachined ultrasound transducer sensors to a neuromorphic resistive memories-based computational map. We present measurement results from the fabricated system comprising resistive memories-based coincidence detectors, delay line circuits, and a full-custom ultrasound sensor. We use these experimental results to calibrate our system-level simulations. These simulations are then used to estimate the angular resolution and energy efficiency of the object localization model. The results reveal the potential of our approach, evaluated in orders of magnitude greater energy efficiency than a microcontroller performing the same task.

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Section: Introductionmentioning

confidence: 99%

Neuromorphic object localization using resistive memories and ultrasonic transducers

et al. 2022

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“…In fact, humans continuously gaze in order to bring the region of interest onto the fovea. In another work 50 , we proposed an eccentricity model for sub-sampling the input visual space similar to that performed by a biological retina. In brief, the periphery of the field of view has coarser resolution than the middle (fovea).…”

Section: Discussionmentioning

confidence: 99%

Event-driven proto-object based saliency in 3D space to attract a robot’s attention

Ghosh

D’Angelo

Glover

et al. 2022

Sci Rep

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To interact with its environment, a robot working in 3D space needs to organise its visual input in terms of objects or their perceptual precursors, proto-objects. Among other visual cues, depth is a submodality used to direct attention to visual features and objects. Current depth-based proto-object attention models have been implemented for standard RGB-D cameras that produce synchronous frames. In contrast, event cameras are neuromorphic sensors that loosely mimic the function of the human retina by asynchronously encoding per-pixel brightness changes at very high temporal resolution, thereby providing advantages like high dynamic range, efficiency (thanks to their high degree of signal compression), and low latency. We propose a bio-inspired bottom-up attention model that exploits event-driven sensing to generate depth-based saliency maps that allow a robot to interact with complex visual input. We use event-cameras mounted in the eyes of the iCub humanoid robot to directly extract edge, disparity and motion information. Real-world experiments demonstrate that our system robustly selects salient objects near the robot in the presence of clutter and dynamic scene changes, for the benefit of downstream applications like object segmentation, tracking and robot interaction with external objects.

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“…The passive and active sensing strategies can both benefit greatly from a closed-loop methodology, especially when an internal model of the system is used to produce informed decisions to update sensor settings and model parameters. Practical examples of such systems include the closed-loop passive sensing techniques of stimulating contrast detection in event-based vision sensors with ego-motion, trading temporal resolution for spatial resolution (Yousefzadeh et al, 2018 ; D'Angelo et al, 2020 ).…”

Section: Different Styles Of Sensingmentioning

confidence: 99%

Neuromorphic Engineering Needs Closed-Loop Benchmarks

Milde

Afshar

et al. 2022

Front. Neurosci.

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Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future.

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Event-Based Eccentric Motion Detection Exploiting Time Difference Encoding

Cited by 27 publications

References 42 publications

Neuromorphic object localization using resistive memories and ultrasonic transducers

Neuromorphic object localization using resistive memories and ultrasonic transducers

Event-driven proto-object based saliency in 3D space to attract a robot’s attention

Neuromorphic Engineering Needs Closed-Loop Benchmarks

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