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

Parallel Processing01:20

Parallel Processing

145
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
145
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
Visual System01:26

Visual System

501
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
501

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

Commissioning of a Monte Carlo-based scanning proton beam for breast cancer: Incorporating LETd calculations and variable RBE models.

Journal of applied clinical medical physics·2026
Same author

Determination of thiram by a peptide-Cu mimetic enzyme with peroxidase-like activity.

Food chemistry·2026
Same author

Ultrahigh-energy gamma-ray emission associated with black hole-jet systems.

National science review·2025
Same author

A Translatable Nanoprodrug Integrates Traditional Chinese and Western Medicines for Chemo-Immunotherapy of Hepatocellular Carcinoma via Ferroptosis.

ACS applied materials & interfaces·2025
Same author

EgrDREB3 from Eucalyptus grandis confer Arabidopsis resilience to low temperature and salinity and senescence delay without growth penalty.

Plant physiology and biochemistry : PPB·2025
Same author

RETRACTED ARTICLE: Implantable neural probes with monolithically integrated CNTFET arrays for multimodal monitoring.

Nature communications·2025
查看所有相关文章

相关实验视频

Updated: Jun 4, 2025

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

466

基于边缘优化和类别感知的多分支语义图像分割模型.

Zhuolin Yang1,2, Zhen Cao1,2, Jianfang Cao1,2

  • 1Department of Computer Science and Technology, Xinzhou Normal University, Xinzhou, China.

PloS one
|December 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了ECMNet,这是一种新的语义图像细分网络,可以提高对象边界的准确性并减少细分混. ECMNet有效地利用多尺度功能和类别信息,以减少参数来提高性能.

更多相关视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

相关实验视频

Last Updated: Jun 4, 2025

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

466
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 图像细分 图像细分

背景情况:

  • 当前的语义图像细分方法经常忽视多尺度特征交互,导致信息丢失和不准确的对象边界细分.
  • 深化卷积层加剧了空间细节的损失,对细分精度产生了负面影响,特别是在物体边缘.

研究的目的:

  • 提出一个边缘优化和类别意识的多分支语义细分网络 (ECMNet),以解决当前细分方法的局限性.
  • 为了提高语义图像分割的准确性,特别是在对象边界,并减少类别混.

主要方法:

  • 一个以注意引导的多分支融合骨干连接不同分辨率的特征并行进行多尺度信息交互.
  • 一个类别感知模块学习类别表示,并使用注意力来指导像素分类的准确性.
  • 一个边缘优化模块可自适应地集成边缘功能,以增强边缘表达和细分.

主要成果:

  • 在Cityscapes数据集上,ECMNet实现了79.2%和CamVid数据集的79.6%的欧盟交叉点 (MIoU) 的平均值.
  • 与现有模型相比,拟建网络的参数数量显著降低.
  • 该方法有效地提高了语义图像细分性能,并解决了部分类别细分混.

结论:

  • ECMNet为语义图像细分提供了一个有前途的方法,实现了高精度和效率.
  • 该网络能够处理多级特征和优化边缘细节,为复杂的细分任务提供了强大的解决方案.
  • 在需要精确图像细分的各种计算机视觉领域,ECMNet显示出显著的应用潜力.