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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

653
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.
653
Deconvolution01:20

Deconvolution

160
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
160
Topographic Surveying and Contours01:29

Topographic Surveying and Contours

96
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...
96
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

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

Rapid diagnosis of aneuploidy using segmental duplication quantitative fluorescent PCR.

PloS one·2014
Same author

[Relationship between activated STAT3 protein and epithelial-mesenchymal transition in papillary thyroid carcinoma].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2014
Same author

The protective effect of vanadium against diabetic cataracts in diabetic rat model.

Biological trace element research·2014
Same author

Fourier spectrum method to determine dose-to-clear in a photoresist.

Optics letters·2014
Same author

Isolation and characterization of polymorphic microsatellites in the genome of yak (Bos grunniens).

Molecular biology reports·2014
Same author

CYLD coordinates with EB1 to regulate microtubule dynamics and cell migration.

Cell cycle (Georgetown, Tex.)·2014

相关实验视频

Updated: Jul 5, 2025

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

全球视觉对象检测使用基于轮线的改进高斯混合模型.

Lei Sun1

  • 1School of Information Engineering, Suqian University, Suqian, Jiangsu, China.

PeerJ. Computer science
|January 23, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的高斯混合模型,用于增强对象检测. 该方法细化前景轮,减少噪声,提高计算机视觉应用中的整体对象检测准确度.

关键词:
功能 聚变的特点 聚变的特点改进了高斯混合模型.对象检测检测对象检测对象检测奥tsu方法的方法.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

542
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

610

相关实验视频

Last Updated: Jul 5, 2025

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
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

542
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

610

科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理

背景情况:

  • 对象检测在计算机视觉中对于识别和定位对象至关重要.
  • 移动物体检测面临着不清楚前景轮和噪音的挑战.

研究的目的:

  • 为了解决移动物体检测的局限性.
  • 为了提高对象轮提取的准确性和减少噪音.

主要方法:

  • 实现了一个改进的高斯混合模型,用于特征融合.
  • 将RGB转换为HSV颜色空间,并建立了一个混合高斯背景模型.
  • 使用背景减去,中位数过,形态处理,以及改进的Canny算法与Otsu值.

主要成果:

  • 成功提取物体轮,精度提高.
  • 显著减少了全球视图中的噪音点和前景中的残留干扰.
  • 在对象轮检测中表现出增强的性能.

结论:

  • 提议的改进的高斯混合模型有效地提高了对象轮精度.
  • 该方法提供了一个强大的解决方案,用于降低对象检测中的噪声.
  • 这种方法有助于在计算机视觉中更精确地检测移动物体.