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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

673
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.
673
Aggregates Classification01:29

Aggregates Classification

327
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
327
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Stereotype Content Model02:16

Stereotype Content Model

14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.7K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

33.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
33.1K

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

Post-prostate Biopsy Septic Shock Due to Extended-Spectrum Beta-Lactamase (ESBL)-Producing Escherichia coli: A Case Report.

Cureus·2026
Same author

Distinct immune landscapes characterize highly versus minimally invasive brain metastases.

JCI insight·2026
Same author

A large positive hysteresis effect for scene categories.

Journal of experimental psychology. Human perception and performance·2026
Same author

A design-integrated framework for neuroarchitectural research.

Frontiers in psychology·2026
Same author

Population receptive field size does not correspond to spatial frequency processing in scene-selective cortex.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Automated diagnosis of usual interstitial pneumonia on chest CT via the mean curvature of isophotes.

European journal of radiology open·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

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 9, 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

620

基于形状的措施改善了场景分类.

Morteza Rezanejad, John Wilder, Dirk B Walther

    IEEE transactions on pattern analysis and machine intelligence
    |December 1, 2023
    PubMed
    概括
    此摘要是机器生成的。

    深度神经网络经常忽略对象轮,与人类不同. 这项研究引入了检测轮线索的算法,通过突出这些未充分利用的视觉特征,显著改善了人类和人工智能模型的场景分类.

    更多相关视频

    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

    11.9K
    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

    9.0K

    相关实验视频

    Last Updated: Jul 9, 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

    620
    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

    11.9K
    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

    9.0K

    科学领域:

    • 计算机视觉 计算机视觉
    • 认知科学 认知科学
    • 人工智能的人工智能

    背景情况:

    • 深度神经网络 (DNN) 显示对颜色和纹理的偏见,在图像分析中忽视了轮信息.
    • 人类擅长使用轮来识别物体和场景,并遵循格斯塔尔特分组原则.
    • 计算模型缺乏用于中级视觉的感知分组规则的实现.

    研究的目的:

    • 开发新的算法来检测复杂场景中的基于轮的线索.
    • 为了计算实现Gestalt分组规则中等水平的视力.
    • 评估轮线索对场景分类准确性的影响.

    主要方法:

    • 开发了用于检测复杂场景中的基于轮线索的算法.
    • 使用中轴转换 (MAT) 基于分组规则来得分轮.
    • 评估场景分类,并没有强调感知分组信息.

    主要成果:

    • 人类观察者和卷积神经网络 (CNN) 模型都在强调感知分组信息时获得了更高的准确性.
    • 与新型措施加权轮相比,与未加权轮相比,加权轮显著提高了CNN模型的性能.
    • 目前的CNN模型似乎没有提取或利用这些基于轮的分组线索,尽管它们很重要.

    结论:

    • 基于轮的感知分组线索对于准确的场景分类至关重要.
    • 格萨特分组规则的计算实现提高了人工智能模型的性能.
    • DNN可能需要修改架构或培训,以更好地利用轮信息.