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

Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

122
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
122
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.1K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.1K
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

13.9K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
13.9K
Cartesian Vector Notation01:28

Cartesian Vector Notation

774
Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
774
Classification of Signals01:30

Classification of Signals

461
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
461
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

19.9K
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
19.9K

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

Graphical Representation of Cavity Length Variations, Δ<i>L</i>, on s-Plane for Low-Finesse Fabry-Pérot Interferometer.

Sensors (Basel, Switzerland)·2025
Same author

Synthesis and Sensing Response of Magnesium Antimoniate Oxide (MgSb<sub>2</sub>O<sub>6</sub>) in the Presence of Propane Atmospheres at Different Operating Voltages.

Sensors (Basel, Switzerland)·2024
Same author

A New Texture Spectrum Based on Parallel Encoded Texture Unit and Its Application on Image Classification: A Potential Prospect for Vision Sensing.

Sensors (Basel, Switzerland)·2023
Same author

Photocatalytic Evaluation and Application as a Sensor for the Toxic Atmospheres (Propane and Carbon Monoxide) of Nickel Antimonate (NiSb<sub>2</sub>O<sub>6</sub>) Powders.

Materials (Basel, Switzerland)·2023
Same author

Synthesis of ZnAl<sub>2</sub>O<sub>4</sub> and Evaluation of the Response in Propane Atmospheres of Pellets and Thick Films Manufactured with Powders of the Oxide.

Sensors (Basel, Switzerland)·2021
Same author

Sensitivity Tests of Pellets Made from Manganese Antimonate Nanoparticles in Carbon Monoxide and Propane Atmospheres.

Sensors (Basel, Switzerland)·2018

相关实验视频

Updated: Jul 2, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.1K

矢量图像表示用于图像分类.

Maria-Eugenia Sánchez-Morales1, José-Trinidad Guillen-Bonilla2, Héctor Guillen-Bonilla3

  • 1Departamento de Ciencias Tecnológicas, Centro Universitario de la Ciénega, Universidad de Guadalajara, Av. Universidad No. 1115, Lindavista, Ocotlán 47810, Jalisco, Mexico.

Journal of imaging
|February 23, 2024
PubMed
概括

本研究介绍了在纹理空间上的矢量图像表示 (VIR-TS),这是使用纹理矢量表示数字图像的新方法. 在图像分类任务中,VIR-TS有效地捕获本地纹理特征.

关键词:
在纹理空间上的矢量图像表示 (VIR-TS)数字图像识别数字图像识别均质的方程系统是同质的方程系统.多类分类器多类分类器质地单位T → 质地单位T

更多相关视频

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K

相关实验视频

Last Updated: Jul 2, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.1K
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K

科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 模式识别 模式识别

背景情况:

  • 数字图像分析通常依赖于提取有意义的特征.
  • 高效地表示复杂的图像纹理是计算机视觉的一个关键挑战.

研究的目的:

  • 提出一种新的转换方法,即在纹理空间上的矢量图像表示 (VIR-TS),用于数字灰度图像.
  • 开发一个新的纹理空间分类器,利用多类识别的拟议转换.

主要方法:

  • 纹理空间上的矢量图像表示 (VIR-TS) 转换将数字图像 (S) 转换为纹理矢量 (C→).
  • 纹理向量 (C→) 封装了局部纹理特征,这些特征来自于解决同质方程系统.
  • 提出了一个新的多类分类器,使用纹理向量 (C→) 作为其特征向量.

主要成果:

  • 在数字树皮图像上的实验部署表明了识别任务的有效性能.
  • 发现分类结果与用于解决同质方程系统的参数值 (λ) 独立.

结论:

  • VIR-TS转换提供了基于纹理的图像表示的有效方法.
  • 拟议的方法显示出在失踪人员检测和医疗图像分析等领域的应用潜力.