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

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.4K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.4K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

644
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.
644
Introduction to GIS01:28

Introduction to GIS

65
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
65

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

The Rise of AI-Generated Anime Avatars: Trends, Challenges, and Opportunities.

IEEE computer graphics and applications·2026
Same author

Hybridizing Expressive Rendering: Stroke-Based Rendering With Classic and Neural Methods.

IEEE computer graphics and applications·2026
Same author

How Visually Literate Are Large Language Models? Reflections on Recent Advances and Future Directions.

IEEE computer graphics and applications·2025
Same author

Enhancing Virtual Reality Training through Artificial Intelligence: A Case Study.

IEEE computer graphics and applications·2025
Same author

The Curse of Performative User Studies.

IEEE computer graphics and applications·2023
Same author

How to Evaluate If Collaborative Augmented Reality Speaks to Its Users.

IEEE computer graphics and applications·2023
Same journal

Graph Pattern Matching based reassembly - 3DGPM.

IEEE computer graphics and applications·2026
Same journal

Making Learning Visible: Turning Public Engagement into Evidence for Academic Learning.

IEEE computer graphics and applications·2026
Same journal

LlymX: Multimodal LLM-Augmented XR for Context-Aware Information Access.

IEEE computer graphics and applications·2026
Same journal

Dynamic Gaussian-Based Digital Twin Reconstruction of Articulated Multi-Joint Objects.

IEEE computer graphics and applications·2026
Same journal

Steiner and Poisson Traversal Initializations: Initial Curve Optimization for Geometric Flow-based Surface Filling.

IEEE computer graphics and applications·2026
Same journal

Insight Into the Insight Toolkit.

IEEE computer graphics and applications·2026
查看所有相关文章

相关实验视频

Updated: Jun 29, 2025

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

954

用于可视化的生成AI:机遇和挑战

Rahul C Basole, Timothy Major, Rahul C Basole

    IEEE computer graphics and applications
    |March 25, 2024
    PubMed
    概括
    此摘要是机器生成的。

    生成型人工智能 (AI) 提供了增强数据可视化的新方法. 这项研究绘制了整个可视化生命周期的AI能力图,确定了这一新兴技术的关键机遇和挑战.

    更多相关视频

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    554
    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.1K

    相关实验视频

    Last Updated: Jun 29, 2025

    Using Generative Art to Convey Past and Future Climate Transitions
    06:10

    Using Generative Art to Convey Past and Future Climate Transitions

    Published on: March 31, 2023

    954
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    554
    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.1K

    科学领域:

    • 计算机科学 计算机科学
    • 信息可视化 信息可视化
    • 人工智能的人工智能

    背景情况:

    • 生成型人工智能 (AI) 和机器学习 (ML) 工具可以创建多种媒体,引发人们对它们在各种领域的应用产生兴趣.
    • 关于AI在数据可视化等领域增强或取代人类活动的潜力存在猜测.
    • 目前还缺乏对生成AI适用于特定可视化任务的清晰理解.

    研究的目的:

    • 分析生成AI在数据可视化生命周期中的适用性.
    • 识别与可视化相关的当前和新兴的生成AI能力.
    • 概述与将生成AI集成到可视化工作流程中相关的机遇和挑战.

    主要方法:

    • 审查当前的生成AI工具和方法.
    • 将AI能力映射到可视化生命周期的不同阶段 (例如,数据准备,视觉编码,交互).
    • 分析案例研究和现场示例.

    主要成果:

    • 生成性AI在各种可视化阶段显示出潜力,从自动图表生成到交互式探索.
    • 当前的能力在内容创建 (文本,图像) 中比在复杂的分析任务中更发达.
    • 挑战包括确保准确性,控制输出,并解决伦理方面的考虑.

    结论:

    • 生成型人工智能为增强可视化过程提供了重大机会,特别是在自动化重复任务和帮助创造性探索方面.
    • 需要进一步的研究来解决局限性,并充分实现AI在先进可视化活动中的潜力.
    • 生成性AI的战略整合可以提高数据可视化实践的效率和创新.