Jove
Visualize
联系我们

相关概念视频

Structural Classification of Joints01:20

Structural Classification of Joints

4.1K
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...
4.1K
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

117
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
117
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

277
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
277
Topographic Surveying and Contours01:29

Topographic Surveying and Contours

253
Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
253
Centroid of a Body: Problem Solving01:03

Centroid of a Body: Problem Solving

1.3K
The centroid of a body is a crucial concept in engineering and physics. Finding the centroid of a body can help determine its stability, its balance point, and even its design. In this context, consider a thin wire bent in the form of a quarter circular arc. Polar coordinates are used to calculate the centroid. The wire is first divided into small differential elements of a length equal to the radius multiplied by the differential angle.
The x-coordinates and y-coordinates of each element's...
1.3K

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

Context-Aware Semantic Localization with Adaptive Sensor Fusion Under Adverse Conditions.

Sensors (Basel, Switzerland)·2026
Same author

An Object-Centric Hierarchical Pose Estimation Method Using Semantic High-Definition Maps for General Autonomous Driving.

Sensors (Basel, Switzerland)·2024
Same author

SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model.

Sensors (Basel, Switzerland)·2023
Same author

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas.

Sensors (Basel, Switzerland)·2022
Same author

3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey.

Sensors (Basel, Switzerland)·2021
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

相关实验视频

Updated: Sep 11, 2025

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

TOSD:一个分层的以对象为中心的描述器,集成形状,颜色和拓.

Jun-Hyeon Choi1, Jeong-Won Pyo2, Ye-Chan An1

  • 1Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了三重物体中心语义描述器 (TOSD),这是一个新的等级框架,用于强大的视觉场景理解. TOSD有效地整合了形状,颜色和拓,在多个抽象级别上改进了对象和场景的表示.

关键词:
功能聚合 功能聚合一个等级描述符.聚合对象的聚合是对象聚合.现场理解 现场理解视觉表示 视觉表示.

更多相关视频

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.2K
Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

2.0K

相关实验视频

Last Updated: Sep 11, 2025

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
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.2K
Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

2.0K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 现有的基于像素和全局特征嵌入方法在捕捉复杂场景语义方面存在局限性.
  • 需要一个描述器框架来支持多层次推理,并集成各种对象属性.
  • 之前在语义建模框架上的工作建立了分层环境表示.

研究的目的:

  • 介绍三重体对象中心语义描述器 (TOSD),这是一个对象中心场景表示的层次框架.
  • 通过整合形状,颜色和拓信息来克服当前视觉特征描述器的局限性.
  • 为了使多层次的推理从低层次的像素细节到高层次的语义结构.

主要方法:

  • 开发了一个分层的以对象为中心的描述器框架 (TOSD).
  • 在对象和场景层面集成形状,颜色和拓信息.
  • 创建了一个具有三个抽象级别的表示:像素,对象和语义结构.

主要成果:

  • 在多个计算机视觉任务中,TOSD表现出了竞争力的表现.
  • 该框架表现出了对阻塞和观点变化等挑战的稳定性.
  • 实现了紧和一致的嵌入,整合了当地线索和全球背景.

结论:

  • TOSD提供了一种统一而有效的方法来对等级化的视觉场景表示.
  • 该方法具有多样性,适用于广泛的视觉和机器人任务.
  • 这项工作通过以对象为中心,多层次的功能集成来推进语义场景理解.