Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.1K
Outliers and Influential Points01:08

Outliers and Influential Points

4.8K
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...
4.8K
Cluster Sampling Method01:20

Cluster Sampling Method

13.2K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.2K
Residual Plots01:07

Residual Plots

5.2K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
5.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.7K
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...
2.7K
What Are Outliers?01:12

What Are Outliers?

4.6K
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...
4.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Exploring anatomical similarity in zero-shot learning for bone abnormality detection.

Scientific reports·2026
Same author

Glissando-Net: Deep Single View Category Level Pose Estimation and 3D Reconstruction.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review.

Diagnostics (Basel, Switzerland)·2024
Same author

On the Synergies Between Machine Learning and Binocular Stereo for Depth Estimation From Images: A Survey.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Zero-Shot Deep Domain Adaptation With Common Representation Learning.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Lifestyle Intervention for Cardiovascular Disease Risk Factors in Jeddah, Saudi Arabia.

Cureus·2020

Related Experiment Video

Updated: Oct 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Inlier Clustering based on the Residuals of Random Hypotheses.

Mohammed Kutbi1, Yizhe Chang2, Philippos Mordohai3

  • 1Department of Computer Science, Saudi Electronic University, Jeddah, Saudi Arabia.

Pattern Recognition Letters
|September 6, 2021
PubMed
Summary

This study introduces Inlier Clustering based on the Residuals of Random Hypotheses (ICR) for motion clustering. ICR effectively identifies pixel correspondences without parameter tuning, offering a robust method for geometric model fitting.

Keywords:
ClusteringModel EstimationMotion Segmentation

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K

Related Experiment Videos

Last Updated: Oct 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Geometric Modeling

Background:

  • Motion clustering is crucial for understanding dynamic scenes.
  • Existing methods often require parameter tuning or prior knowledge of cluster numbers.
  • Robust geometric model fitting necessitates accurate identification of inlier data points.

Purpose of the Study:

  • To introduce a novel motion clustering approach, Inlier Clustering based on the Residuals of Random Hypotheses (ICR).
  • To develop a supervised recursive formulation (r-ICR) that does not require the number of clusters to be known beforehand.
  • To provide a parameter-free and robust method for motion clustering and geometric model fitting.

Main Methods:

  • Generating a signature for pixel correspondences using residuals from random model hypotheses.
  • Identifying cluster members based on correlated residuals.
  • Utilizing a supervised recursive formulation for scenarios where cluster numbers are unknown.

Main Results:

  • ICR effectively clusters motion based on residual correlations.
  • The approach demonstrates robustness without requiring inlier-outlier thresholds or parameter tuning.
  • r-ICR successfully handles motion clustering without prior knowledge of the number of clusters when training data is available.

Conclusions:

  • ICR offers a novel and advantageous approach to motion clustering.
  • The method's parameter-free nature simplifies its application.
  • r-ICR extends the utility of ICR for supervised learning tasks with unknown cluster counts.