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

Outliers and Influential Points01:08

Outliers and Influential Points

4.1K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.1K
What Are Outliers?01:12

What Are Outliers?

3.9K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
3.9K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.2K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.2K
Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

44
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
44
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.1K

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

Weakly-Supervised Shape Multi-Completion of Point Clouds by Structural Decomposition.

IEEE transactions on visualization and computer graphics·2025
Same author

TGSL: Trade-off graph structure learning via multifaceted graph information bottleneck.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Parameterize Structure With Differentiable Template for 3D Shape Generation.

IEEE transactions on visualization and computer graphics·2025
Same author

Graph Neural Networks with Coarse- and Fine-Grained Division for mitigating label noise and sparsity.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

When an extra rejection class meets out-of-distribution detection in long-tailed image classification.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Are transformer-based models more robust than CNN-based models?

Neural networks : the official journal of the International Neural Network Society·2024
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
Same journal

Spatial-temporal Relation guided Motion Transfer via Diffusion Model.

IEEE transactions on visualization and computer graphics·2026
查看所有相关文章

相关实验视频

Updated: Jul 12, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

部分点云与异常值的协作完成和细分.

Changfeng Ma, Yang Yang, Jie Guo

    IEEE transactions on visualization and computer graphics
    |October 30, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了CS-Net,这是一种用于3D点云处理的新方法,它协同使用完成和细分来有效地处理异常值,而无需预处理. 该方法显著提高了异常值的稳定性和完成几何任务的准确性.

    更多相关视频

    Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates
    11:24

    Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

    Published on: March 7, 2017

    7.0K
    Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
    08:50

    Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

    Published on: February 9, 2019

    7.7K

    相关实验视频

    Last Updated: Jul 12, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K
    Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates
    11:24

    Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

    Published on: March 7, 2017

    7.0K
    Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
    08:50

    Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

    Published on: February 9, 2019

    7.7K

    科学领域:

    • 计算机视觉 计算机视觉
    • 几何深度学习 几何深度学习
    • 3D数据处理 3D数据处理

    背景情况:

    • 在3D扫描点云中的异常值降低了几何任务中的性能.
    • 现有的方法通常需要无异常值的数据或单独的异常值去除步骤.
    • 在异常值处理中,点云完成和细分之间的协同方法尚未被探索.

    研究的目的:

    • 调查点云完成和细分之间的相互促进,以进行强大的异常值处理.
    • 为具有异常值的部分点云提出一个新的协作网络,CS-Net.
    • 开发一个集成完成和细分的学习范式,而不需要预处理.

    主要方法:

    • 提出了一个具有级联架构的协作完成和细分网络 (CS-Net).
    • 采用了一种新的完成网络,利用细分标签和最远点采样来净化点云.
    • 在完成模块中利用KNN分组进行增强生成.
    • 开发了具有异常值的部分点云的基准数据集.

    主要成果:

    • 与现有方法相比,CS-Net在异常值稳定性方面取得了显著的改进.
    • 协作方法通过使用更清洁,过的点云来提高完成准确性.
    • 通过利用完成模块推断的完整形状来提高细分精度.
    • 广泛的实验验证了拟议方法和数据集的有效性.

    结论:

    • 完成和细分网络的协作机制有效地击败了3D点云中的异常值.
    • CS-Net为处理具有固有的异常值的部分点云提供了强大而准确的解决方案.
    • 拟议的基准数据集有助于进一步研究强大的3D点云分析.