Utility and Feasibility of a Center Surround Event Camera

• URL: http://arxiv.org/abs/2202.13076v1
• Date: Sat, 26 Feb 2022 06:32:54 GMT
• Title: Utility and Feasibility of a Center Surround Event Camera
• Abstract summary: This paper proposes a method of implementing a compact, energy-efficient Center Surround DVS with a surround smoothing network. The CSDVS would significantly reduce events caused by low spatial frequencies, but amplify the informative high frequencytemporal events.
• Score: 1.911678487931003
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Standard dynamic vision sensor (DVS) event cameras output a stream of spatially-independent log-intensity brightness change events so they cannot suppress spatial redundancy. Nearly all biological retinas use an antagonistic center-surround organization. This paper proposes a practical method of implementing a compact, energy-efficient Center Surround DVS (CSDVS) with a surround smoothing network that uses compact polysilicon resistors for lateral resistance. The paper includes behavioral simulation results for the CSDVS (see sites.google.com/view/csdvs/home). The CSDVS would significantly reduce events caused by low spatial frequencies, but amplify the informative high frequency spatiotemporal events.

Related papers

• FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection [0.0]
  Event cameras offer inherent advantages in high-speed and high-dynamic-range scenarios.<n>Current approaches partition the accumulation window into fixed temporal sub-bins.<n>We propose FATE, a unified framework built upon a novel spatial framework.
  arXiv  Detail & Related papers  (2026-06-15T22:32:09Z)
• FS-DVS: A Frequency-Selective Dynamic Visual Sensing Paradigm for Enhancing Information Completeness [30.541053618924277]
  Dynamic vision sensors (DVS) offer exceptional temporal resolution and dynamic range by asynchronously reporting pixel-level intensity changes.<n>We propose FS-DVS (Frequency-Selective Dynamic Vision Sensor), a novel paradigm that integrates a learnable spatial filter.<n>By developing a differentiable event simulation framework, the spatial filter can be optimized end-to-end with downstream tasks.
  arXiv  Detail & Related papers  (2026-06-05T03:00:36Z)
• EventGait: Towards Robust Gait Recognition with Event Streams [50.890621860023]
  Event cameras offer microsecond temporal resolution and high dynamic range, naturally capturing robust dynamic cues and suppressing static noise.<n>We propose textbfEventGait, an end-to-end dual-stream framework that separately models motion and shape while preserving the advantages of events.<n>Our approach sets a new state of the art on both synthesized and real-world event-based gait benchmarks, highlighting the robustness and potential of event-driven gait analysis.
  arXiv  Detail & Related papers  (2026-05-21T08:12:04Z)
• SoLAR: Error-Resilient Streamable Long-Horizon Free-Viewpoint Video Reconstruction with Anchor Activation and Latent Recalibration [57.159190580279585]
  Motivated by bit allocation theory, we analyze dynamic-anchor-based volumetric video representation within a rate-distortion optimization framework.<n>We propose textbfSoLAR, which is the first error-resilient streamable FVV framework that maintains stable reconstruction quality on long sequences.
  arXiv  Detail & Related papers  (2026-05-08T06:48:59Z)
• Spatio-Temporal Difference Guided Motion Deblurring with the Complementary Vision Sensor [12.418360718687026]
  Motion blur arises when rapid scene changes occur during the exposure period, collapsing rich intra-exposure motion into a single RGB frame.<n>We propose Spatio-Temporal Difference Guided Deblur Net (STGDNet) to restore structure and color details lost in blurry RGB inputs.<n>Our method outperforms current RGB or event-based approaches in both synthetic CVS dataset and real-world evaluations.
  arXiv  Detail & Related papers  (2026-04-12T09:54:14Z)
• OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers [68.73273027298625]
  megaconstellations are driving the long-term vision of space data centers (SDCs)<n>AirComp is an in-network aggregation framework for learning free-space (FSO)<n>AirVote integrates sign gradient (SGD) with a majority-signposition modulation (PPM), where each satellite conveys local gradient by activating PPM time slots.<n>OptiVote mitigates phase-sensitive field superposition into phase-agnostic optical intensity combining.
  arXiv  Detail & Related papers  (2025-12-30T16:40:02Z)
• HAD: Hierarchical Asymmetric Distillation to Bridge Spatio-Temporal Gaps in Event-Based Object Tracking [80.07224739976911]
  Event cameras offer exceptional temporal resolution and a range (modal)<n> RGB cameras excel at capturing rich texture with high resolution, whereas event cameras offer exceptional temporal resolution and a range (modal)
  arXiv  Detail & Related papers  (2025-10-22T13:15:13Z)
• SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos [50.51658520045165]
  Spike-d Optimal TrAnsport Saliency Region Detection (SOTA) is a framework that leverages the strengths of spike cameras while mitigating biases in both spatial and temporal dimensions. Our method introduces Spike-based Micro-debias (SM) to capture subtle frame-to-frame variations. SOTA refines predictions by reducing inconsistencies across diverse conditions.
  arXiv  Detail & Related papers  (2025-05-01T08:30:40Z)
• FreSca: Scaling in Frequency Space Enhances Diffusion Models [55.75504192166779]
  This paper explores frequency-based control within latent diffusion models.<n>We introduce FreSca, a novel framework that decomposes noise difference into low- and high-frequency components.<n>FreSca operates without any model retraining or architectural change, offering model- and task-agnostic control.
  arXiv  Detail & Related papers  (2025-04-02T22:03:11Z)
• Characterization of dim light response in DVS pixel: Discontinuity of event triggering time [7.623462728752422]
  We analyze the typical Dynamic Vision Sensors (DVS) circuit, and find that there exists discontinuity of event triggering time. We point out that the discontinuity depends exclusively on the changing speed of light intensity.
  arXiv  Detail & Related papers  (2024-04-27T03:55:53Z)
• TBSN: Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising [94.09442506816724]
  Blind-spot networks (BSN) have been prevalent network architectures in self-supervised image denoising (SSID) We present a transformer-based blind-spot network (TBSN) by analyzing and redesigning the transformer operators that meet the blind-spot requirement. For spatial self-attention, an elaborate mask is applied to the attention matrix to restrict its receptive field, thus mimicking the dilated convolution. For channel self-attention, we observe that it may leak the blind-spot information when the channel number is greater than spatial size in the deep layers of multi-scale architectures.
  arXiv  Detail & Related papers  (2024-04-11T15:39:10Z)
• Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation [54.64335350932855]
  We propose a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging. Our proposed Asyn system integrates the Dynamic Vision Sensors (DVS) with a set of LCD panels. The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams.
  arXiv  Detail & Related papers  (2024-03-14T13:45:09Z)
• An Event-Oriented Diffusion-Refinement Method for Sparse Events Completion [36.64856578682197]
  Event cameras or dynamic vision sensors (DVS) record asynchronous response to brightness changes instead of conventional intensity frames. We propose an inventive event completion sequence approach conforming to unique characteristics of event data in both the processing stage and the output form. Specifically, we treat event streams as 3D event clouds in thetemporal domain, develop a diffusion-based generative model to generate dense clouds in a coarse-to-fine manner, and recover exact timestamps to maintain the temporal resolution of raw data successfully.
  arXiv  Detail & Related papers  (2024-01-06T08:09:54Z)
• V2CE: Video to Continuous Events Simulator [1.1009908861287052]
  We present a novel method for video-to-events stream conversion from multiple perspectives, considering the specific characteristics of Dynamic Vision Sensor (DVS) A series of carefully designed timestamp losses helps enhance the quality of generated event voxels significantly. We also propose a novel local dynamic-aware inference strategy to accurately recover event timestamps from event voxels in a continuous fashion.
  arXiv  Detail & Related papers  (2023-09-16T06:06:53Z)
• Alignment-free HDR Deghosting with Semantics Consistent Transformer [76.91669741684173]
  High dynamic range imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. Existing methods often focus on the spatial misalignment across input frames caused by the foreground and/or camera motion. We propose a novel alignment-free network with a Semantics Consistent Transformer (SCTNet) with both spatial and channel attention modules.
  arXiv  Detail & Related papers  (2023-05-29T15:03:23Z)
• EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks [107.62975594230687]
  We demonstrate an application where event cameras excel: accurately estimating the impact location of fast-moving objects. We introduce a lightweight event representation called Binary Event History Image (BEHI) to encode event data at low latency. We show that the system is capable of achieving a success rate of 81% in catching balls targeted at different locations, with a velocity of up to 13 m/s even on compute-constrained embedded platforms.
  arXiv  Detail & Related papers  (2023-04-14T15:23:28Z)
• Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events [63.984927609545856]
  Event-based Inter/intra-frame Compensator (E-IC) is proposed to predict the per-pixel dynamic between arbitrary time intervals. We show that the proposed method achieves state-of-the-art and shows remarkable performance for event-based RS2GS inversion in real-world scenarios.
  arXiv  Detail & Related papers  (2023-04-14T05:30:02Z)
• A Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network [0.2578242050187029]
  A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. This paper proposes a novel deep learning-based compression scheme for event data.
  arXiv  Detail & Related papers  (2022-10-27T12:21:14Z)
• ESL: Event-based Structured Light [62.77144631509817]
  Event cameras are bio-inspired sensors providing significant advantages over standard cameras. We propose a novel structured-light system using an event camera to tackle the problem of accurate and high-speed depth sensing.
  arXiv  Detail & Related papers  (2021-11-30T15:47:39Z)
• Naturalizing Neuromorphic Vision Event Streams Using GANs [0.0]
  One of the challenges associated with neuromorphic vision is the lack of interpretability of event streams. In this paper, we apply the Pix2Pix network to naturalize the event stream from spike-converted CIFAR-10 and Linnaeus 5 datasets. The quality of the network is benchmarked by performing image classification of naturalized event streams.
  arXiv  Detail & Related papers  (2021-02-14T20:48:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.