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

Association Areas of the Cortex01:21

Association Areas of the Cortex

5.3K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.3K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

651
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
651
Parallel Processing01:20

Parallel Processing

151
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...
151
Visual System01:26

Visual System

582
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...
582

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

CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics-Based Appearance-Medium Decoupling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Bio-heterojunction-engineered recombinant collagen hydrogel orchestrates multimodal sterilization and immunomodulation for MRSA-infected wound healing.

Bioactive materials·2026
Same author

Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Coupled Diffusion Posterior Sampling for Unsupervised Hyperspectral and Multispectral Images Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jul 2, 2025

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
13:01

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

33.9K

一个用于多模态图像融合的通用空间频率学习框架.

Man Zhou, Jie Huang, Keyu Yan

    IEEE transactions on pattern analysis and machine intelligence
    |February 21, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了SFINet,这是一个新型的多式联网图像融合网络,集空间和频率领域. 通过利用本地和全球信息,SFINet可以提高像面利和深度超分辨率等任务的图像质量.

    更多相关视频

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K
    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
    08:49

    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

    Published on: December 1, 2023

    1.4K

    相关实验视频

    Last Updated: Jul 2, 2025

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
    13:01

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

    Published on: April 10, 2016

    33.9K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K
    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
    08:49

    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

    Published on: December 1, 2023

    1.4K

    科学领域:

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

    背景情况:

    • 多模式图像融合,包括泛利和深度超分辨率,旨在通过结合来自不同来源的信息来生成高分辨率图像.
    • 现有的方法主要集中在空间域处理上,忽视了对于高频信息重建至关重要的固有频域连接.
    • 这种差距限制了改进核聚变性能的潜力,因为不能充分利用互补数据特征.

    研究的目的:

    • 解决多式影像融合中的仅空间域方法的局限性.
    • 提出新的解决方案,有效地整合空间和频域信息,以增强图像融合.
    • 开发和验证新的网络架构,SFINet及其改进版本SFINet++,用于图像融合任务的卓越性能.

    主要方法:

    • 设计了空间频率信息整合网络 (SFINet),包括具有双域互动的空间和频率域分支.
    • 空间分支使用具有空间卷积的可逆神经运算符来实现局部信息集成.
    • 频分支采用模态感知深度里埃转换来捕获全球上下文信息;SFINet++通过无信息损失的可逆神经运算符来增强空间表示.

    主要成果:

    • 广泛的实验证明了SFINet和SFINet++在多式联动图像融合中的有效性.
    • 与最先进的方法相比,拟议的网络实现了卓越的性能.
    • 验证是在两个代表性的任务上进行的:面尖和深度超分辨率.

    结论:

    • 整合空间和频域信息在多模式图像融合中提供了显著的优势.
    • SFINet和SFINet++为重建高频信息和提高图像质量提供了有效的解决方案.
    • 拟议的双域方法推动了图像融合领域的发展,在具有挑战性的应用中提供了卓越的结果.