Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Vision01:24

Vision

52.9K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.9K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Comparative Prognostic Assessment of EKFC Equations for Mortality Prediction: A Population-based Cohort Study.

Cardiorenal medicine·2026
Same author

Pharmacokinetic/pharmacodynamic target attainment of eravacycline in a liver transplant recipient with refractory VRE <i>faecium</i> infection.

Antimicrobial agents and chemotherapy·2026
Same author

Bibliometric Analysis of White Matter Disease and Novel Drug Therapy.

Current neuropharmacology·2026
Same author

Comparison of epidural anesthesia and intravenous self-control analgesia on postoperative recovery quality in duodenectomy.

World journal of gastrointestinal surgery·2026
Same author

Stepwise De-solvation and Diffusion Kinetics of Hydrated Zn-ion in Hierarchical Porous Carbon Anode for Improved EDLC Behavior.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A disproportionality analysis of adverse events associated with omadacycline based on the FDA adverse event reporting system database.

The Journal of antimicrobial chemotherapy·2026

相关实验视频

Updated: May 24, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

515

EvRepSL:通过自我监督学习进行事件流表示,以实现基于事件的视觉.

Qiang Qu, Xiaoming Chen, Yuk Ying Chung

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一种数据驱动的方法,以改善事件摄像头的事件流表示. 新的方法EvRepSL提高了计算机视觉任务的数据质量,而不需要手工设计.

    更多相关视频

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    451

    相关实验视频

    Last Updated: May 24, 2025

    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
    06:25

    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

    Published on: February 23, 2024

    515
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    451

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 基于事件的传感.

    背景情况:

    • 事件流表示对于使用事件摄像头的计算机视觉任务至关重要.
    • 目前的方法依赖于手动设计,由于噪音事件数据导致质量变化.

    研究的目的:

    • 开发一种数据驱动的方法来增强事件流表示.
    • 提高基于事件的计算机视觉数据的质量和可靠性.

    主要方法:

    • 介绍了EvRep,一种基于时空统计的新型事件流表示.
    • 导出了异步事件流与同步之间的关系.
    • 训练了一个使用EvRep的自我监督表示生成器 (RepGen) 来创建EvRepSL.

    主要成果:

    • 与现有表示方式相比,EvRepSL表现出更高的性能.
    • 该方法在不同的事件摄像头和任务 (分类,光流) 中显示了多功能性.
    • 在没有微调或再培训的情况下实现了高质量的表示.

    结论:

    • 拟议的数据驱动方法显著提高了事件流表示质量.
    • EvRepSL为基于事件的计算机视觉提供了一个强大而通用的解决方案.
    • 这项工作为更可靠的事件摄像机应用铺平了道路.